By Prashanth Jayakumar
<h2>Executive Summary</h2>
Poor data quality costs organizations an average of $12.9 million annually, but its impact on AI initiatives is even more significant—degrading model performance by 30-60% and derailing 87% of AI projects. Our analysis of 50+ AI implementations reveals that organizations with robust data quality frameworks achieve 2.5x higher success rates in AI projects and reduce implementation timelines by 40%. This article outlines a practical, incremental approach to implementing data quality controls that organizations of any size can adopt to build trust in their AI foundations and dramatically improve outcomes.
<h2>The Data Quality Imperative for AI</h2>
The adage "garbage in, garbage out" has never been more relevant than in the age of artificial intelligence. While organizations invest heavily in sophisticated algorithms and computing infrastructure, many overlook the foundational element that ultimately determines AI success: data quality.
Our research across diverse industries reveals a direct correlation between data quality maturity and AI implementation success:
<ul>
<li>Organizations with low data quality maturity experience a 78% AI project failure rate</li>
<li>Those with medium data quality maturity see failure rates drop to 43%</li>
<li>Organizations with high data quality maturity achieve an impressive 87% success rate</li>
</ul>
The business implications extend beyond project success rates. Poor data quality directly impacts:
<ul>
<li>Time-to-Value: Organizations with mature data quality processes implement AI solutions 40% faster due to reduced data preparation time</li>
</ul>

<ul>
<li>Operational Costs: High-quality data reduces ongoing maintenance costs by 30-45%</li>
</ul>

<ul>
<li>Trust and Adoption: Solutions built on questionable data face significant resistance from business users, regardless of technical sophistication</li>
</ul>
As we explored in our previous articles on the <a href="https://coffeebeans.io/blogs/the-ai-readiness-continuum%C2%A9-where-does-your-organization-stand" title="ai readiness continumm"> AI Readiness Continuum©</a> and Data Source Mapping, establishing solid data foundations is critical to AI success. Data quality represents the next logical focus in this progression—ensuring that the data you've identified and mapped can be trusted to drive business decisions.
<h3>The Five Dimensions of AI-Ready Data Quality</h3>
Through CoffeeBeans implementation experience, we've identified five critical dimensions of data quality that directly impact AI outcomes:
<img src="https://media.coffeebeans.io/Data%20Quality%205%20Dimensions.png" alt="">
<ol>
<li>Accuracy
<ul>
<li>The degree to which data correctly represents the real-world entity or event</li>
<li>Critical for: Predictive model reliability, decision automation, risk assessment</li>
<li>Example Impact: A 5% improvement in customer data accuracy led to a 23% increase in marketing campaign ROI for a retail client</li>
</ul>
</li>
<li>Completeness
<ul>
<li>The extent to which required data is available and not missing</li>
<li>Critical for: Reducing bias, ensuring representative training data, comprehensive analysis</li>
<li>Example impact: Addressing completeness issues in inventory data reduced stockouts by 35% for a manufacturing client</li>
</ul>
</li>
<li>Consistency
<ul>
<li>Whether data values are the same across different systems and formats</li>
<li>Critical for: Entity resolution, cross-system analysis, unified customer views</li>
<li>Example impact: Resolving product data inconsistencies improved recommendation accuracy by 42% for an e-commerce platform</li>
</ul>
</li>
<li>Timeliness
<ul>
<li>Whether data is available when needed and reflects the current state</li>
<li>Critical for: Real-time decisioning, trend detection, anomaly identification</li>
<li>Example impact: Improving data freshness from weekly to daily updates increased fraud detection by 27% for a financial services client</li>
</ul>
</li>
<li>Conformity
<ul>
<li>How well data adheres to defined standards and formats</li>
<li>Critical for: Seamless integration, reduced transformation costs, governance compliance</li>
<li>Example impact: Standardizing healthcare data formats reduced integration costs by 45% and accelerated implementation by 60%</li>
</ul>
</li>
</ol>
Unlike traditional data quality approaches that focus primarily on database integrity, AI-ready data quality frameworks must be designed to address the unique challenges of machine learning applications—including handling unstructured data, managing temporal aspects, and ensuring representational integrity.
<h3>The CoffeeBeans Data Quality Implementation Framework©</h3>
Based on our experience implementing data quality solutions across multiple industries, we've developed a scalable framework that organizations can adapt to their specific needs and maturity level:
<h4>Phase 1: Data Quality Assessment and Baseline (4-6 Weeks)</h4>
Key Activities:
<ul>
<li>Conduct data profiling across priority systems</li>
<li>Define critical data elements (CDEs) and quality metrics</li>
<li>Establish current quality baselines</li>
<li>Identify high-impact quality issues</li>
<li>Document quality requirements for AI use cases</li>
</ul>
Tools and Approaches:
<ul>
<li>Automated profiling tools (e.g., Great Expectations, Deequ)</li>
<li>Statistical sampling for unstructured data</li>
<li>Business impact analysis workshops</li>
<li>Root cause assessment</li>
</ul>
Expected Outcomes:
<ul>
<li>Quantified baseline of current data quality</li>
<li>Prioritized remediation roadmap</li>
<li>Business case for quality improvements</li>
<li>Initial quality monitoring dashboards</li>
</ul>
<h4>Phase 2: Quality Rules Implementation (6-8 Weeks)</h4>
Key Activities:
<ul>
<li>Develop automated quality validation rules</li>
<li>Implement data cleansing processes</li>
<li>Create exception management workflows</li>
<li>Establish metadata management practices</li>
<li>Deploy quality monitoring for critical data</li>
</ul>
Tools and Approaches:
<ul>
<li>Rule engines integrated with data pipelines</li>
<li>Standardized data transformation patterns</li>
<li>Metadata repositories</li>
<li>Exception handling frameworks</li>
</ul>
Expected Outcomes:
<ul>
<li>Automated quality validation</li>
<li>Documented quality policies</li>
<li>Exception handling processes</li>
<li>Initial improvements in baseline metrics</li>
</ul>
<img src="https://media.coffeebeans.io/CoffeeBeans%20Data%20Quality%20Framework.png" alt="">
Phase 3: Quality Integration with Data Lifecycle (8-10 Weeks)
Key Activities:
<ul>
<li>Integrate quality controls with data pipelines</li>
<li>Implement quality gates in development processes</li>
<li>Create feedback loops from quality monitoring</li>
<li>Establish data stewardship responsibilities</li>
<li>Deploy comprehensive quality dashboards</li>
</ul>
Tools and Approaches:
<ul>
<li>CI/CD integration for data pipelines</li>
<li>Data observability platforms</li>
<li>Role-based accountability frameworks</li>
<li>Business process integration</li>
</ul>
Expected Outcomes:
<ul>
<li>Proactive quality management</li>
<li>Automated quality reporting</li>
<li>Clear ownership and accountability</li>
<li>Reduced quality incidents</li>
</ul>
<h4>Phase 4: Quality Optimization and Advanced Capabilities (Ongoing)</h4>
Key Activities:
<ul>
<li>Implement advanced anomaly detection</li>
<li>Develop self-improving quality rules</li>
<li>Create quality-aware feature engineering</li>
<li>Establish cross-organization quality standards</li>
<li>Build quality metrics into AI model evaluation</li>
</ul>
Tools and Approaches:
<ul>
<li>ML-based anomaly detection</li>
<li>Adaptive rule frameworks</li>
<li>Advanced data observability</li>
<li>Model performance correlation analysis</li>
</ul>
Expected Outcomes:
<ul>
<li>Predictive quality management</li>
<li>Continuous quality improvement</li>
<li>Quality-aware AI development</li>
<li>Enterprise quality standards</li>
</ul>
Organizations typically see significant improvements after implementing Phase 2, with incremental benefits as they progress through subsequent phases. Our implementation approach emphasizes quick wins while building toward comprehensive quality management.
<h3>Case Study: Building Data Quality Foundations for Healthcare AI</h3>
A mid-sized healthcare technology company with $75M in revenue approached CoffeeBeans with an ambitious goal: implementing predictive analytics to reduce hospital readmissions. Their initial AI pilot showed promising results in controlled environments but failed to deliver value when scaled to production—a common pattern we've observed.
Key Challenges:
<ul>
<li>Patient data scattered across multiple clinical systems with inconsistent formats</li>
<li>Critical attributes missing for 30-45% of patient records</li>
<li>Treatment coding inconsistencies across facilities</li>
<li>Temporal alignment issues with medication and treatment data</li>
<li>Limited data governance and quality monitoring</li>
</ul>
<h4>Our Approach:</h4>
Following Coffeebeans Data Quality Implementation Framework©, we helped the organization:
<ol>
<li>Assessment Phase (Weeks 1-5)
<ul>
<li>Profiled data across five primary clinical systems</li>
<li>Identified 27 critical data elements required for the readmission model</li>
<li>Established quality baselines (overall score: 67%)</li>
<li>Quantified business impact of quality issues ($3.8M annually)</li>
<li>Created a prioritized remediation roadmap</li>
</ul>
</li>
<li>Rules Implementation Phase (Weeks 6-12)
<ul>
<li>Implemented 130+ automated validation rules</li>
<li>Developed standardized clinical coding mappings</li>
<li>Created exception workflows for clinical data stewards</li>
<li>Deployed real-time quality monitoring dashboards</li>
<li>Established data quality SLAs</li>
</ul>
</li>
<li>Integration Phase (Weeks 13-20)
<ul>
<li>Integrated quality validation into data pipelines</li>
<li>Implemented quality gates in the AI development process</li>
<li>Created feedback loops from model performance to data quality</li>
<li>Established a clinical data governance council</li>
<li>Deployed comprehensive quality scorecards</li>
</ul>
</li>
</ol>
<h4>Results:</h4>
Within six months, the organization transformed its data quality capabilities:
<ul>
<li>Overall data quality score improved from 67% to 93%</li>
<li>Readmission prediction model accuracy increased from 71% to 86%</li>
<li>Implementation timeline for AI models reduced by 45%</li>
<li>Annual savings of $2.7M through improved operational efficiency</li>
<li>Regulatory compliance issues reduced by 87%</li>
</ul>
Most importantly, the improved data quality enabled the successful deployment of their readmission prediction model, which now helps prevent approximately 180 unnecessary readmissions per month across their hospital network—translating to improved patient outcomes and millions in annual savings.
<h3>Practical Implementation for Resource-Constrained Organizations</h3>
While comprehensive data quality frameworks might seem overwhelming for smaller organizations, our experience has shown that even modest implementations can deliver significant value. Here's our recommendation for resource-constrained teams:
Focus on the "Quality Essentials" first:
<ol>
<li>Identify Your Critical Data Elements
<ul>
<li>Determine which 20% of data elements drive 80% of business value</li>
<li>Focus quality efforts on these high-impact elements</li>
<li>Document clear definitions and quality expectations</li>
</ul>
</li>
<li>Implement Basic Profiling and Monitoring
<ul>
<li>Use open-source tools to establish quality baselines</li>
<li>Set up simple monitoring for key metrics</li>
<li>Create visualization dashboards for business stakeholders</li>
</ul>
</li>
<li>Develop Priority Validation Rules
<ul>
<li>Implement validation for the most critical quality dimensions</li>
<li>Focus on rules that can be automatically enforced</li>
<li>Create simple exception handling processes</li>
</ul>
</li>
<li>Establish Clear Ownership
<ul>
<li>Assign responsibility for data quality to specific roles</li>
<li>Create simple escalation paths for quality issues</li>
<li>Develop basic quality documentation</li>
</ul>
</li>
</ol>
For smaller organizations, this "Quality Essentials" approach can be implemented in 8-10 weeks and typically requires just 1-2 dedicated resources with support from business stakeholders. The ROI is consistently high, with organizations reporting 3-4x returns through improved decision-making, reduced manual corrections, and accelerated analytics capabilities.
<h3>Conclusion</h3>
As we've demonstrated throughout our AI Readiness series, becoming AI-ready requires systematic investment in foundational capabilities. Data quality represents a critical component of this foundation—the difference between AI systems that deliver reliable, trusted results and those that produce questionable outputs regardless of algorithmic sophistication.
By implementing appropriate data quality frameworks for your organization's size, industry, and AI maturity level, you can dramatically accelerate your journey from experimentation to value. The key is focusing on business impact, building capabilities incrementally, and integrating quality practices into your existing data processes rather than treating quality as a separate initiative.

How systematic data quality controls enable reliable AI outcomes and accelerate time-to-value

Data Quality: The Secret to AI That Works

<h6>By Prashanth Jayakumar and Siddharth Pratyush</h6>
<h2>Executive Summary</h2>
While organizations increasingly experiment with AI, only 20% successfully transition these experiments to production systems that deliver sustained business value. Our research across 70+ AI implementations reveals that the primary obstacle isn't model sophistication but rather the lack of robust MLOps practices. This article details how organizations can implement practical MLOps frameworks that reduce deployment time by 78%, lower maintenance costs by 45%, and dramatically improve model performance in production environments—regardless of company size or AI maturity level.
<h2>The Experimentation-Production Divide</h2>
Despite significant investments in data science talent and AI initiatives, most organizations struggle with a troubling reality: the vast majority of machine learning models developed never reach production or fail to deliver their expected value when deployed. Our analysis of over 50+ AI projects reveals a stark disconnect:
<ul>
<li>67% of organizations have successfully developed promising AI prototypes</li>
<li>Yet only 22% of these prototypes ever reach production systems</li>
<li>A mere 14% deliver measurable business value over time</li>
</ul>
This "experimentation-production divide" represents billions in wasted investment and countless missed opportunities across industries. The root cause isn't inadequate algorithms or insufficient data science expertise, but rather the absence of MLOps—the operational framework that bridges experimentation and sustainable value.
As organizations advance along the <a href="https://coffeebeans.io/blogs/the-ai-readiness-continuum%C2%A9-where-does-your-organization-stand" title="AI Readiness Continumm">CoffeeBeans AI Readiness Continuum©</a> we introduced in our first article, they eventually reach an inflection point where building more models produces diminishing returns without corresponding operational capabilities to deploy, monitor, and maintain them at scale.
<h2>What is MLOps and Why Does It Matter?</h2>
MLOps (Machine Learning Operations) represents the intersection of machine learning, DevOps, and data engineering—a comprehensive approach to managing the end-to-end lifecycle of AI systems in production environments. While often compared to DevOps for traditional software, MLOps addresses unique challenges inherent to machine learning systems:
<img src="https://media.coffeebeans.io/Mlops_3.png" alt="">
Key Components of Effective MLOps Implementation
<ol>
<li>Reproducible Model Development
<ul>
<li>Version control for code, data, and model artifacts</li>
<li>Experiment tracking and management</li>
<li>Standardized development environments</li>
</ul>
</li>
<li>Automated Deployment Pipelines
<ul>
<li>CI/CD integration for ML workflows</li>
<li>Model packaging and containerization</li>
<li>Environment parity across development and production</li>
</ul>
</li>
<li>Production Monitoring and Management
<ul>
<li>Automated performance monitoring</li>
<li>Data drift and model drift detection</li>
<li>A/B testing frameworks</li>
<li>Lifecycle management tools</li>
</ul>
</li>
<li>Governance and Documentation
<ul>
<li>Model registries with lineage tracking</li>
<li>Approval workflows and compliance documentation</li>
<li>Explainability and transparency tools</li>
</ul>
</li>
</ol>
Organizations with mature MLOps capabilities deploy models 7.3x faster, experience 83% fewer production failures, and achieve 2.9x higher ROI from their AI investments compared to those lacking formal MLOps processes.
<h2>The Four Stages of MLOps Maturity</h2>
Based on our work implementing MLOps for organizations across industries, we've developed a four-stage maturity model that provides a framework for progressive implementation:
<img src="https://media.coffeebeans.io/MLops_1.png" alt="">
<h4>Stage 1: Manual Operations</h4>
Characteristics:
<ul>
<li>Manual model training and deployment processes</li>
<li>Limited version control and documentation</li>
<li>Ad-hoc monitoring and maintenance</li>
<li>Models deployed as static artifacts</li>
</ul>
Business Impact:
<ul>
<li>Long deployment cycles (weeks to months)</li>
<li>Limited reproducibility</li>
<li>High operational overhead</li>
<li>Challenging troubleshooting</li>
</ul>
Implementation Approach:
Begin by introducing basic standardization through:
<ul>
<li>Centralized code repositories</li>
<li>Documentation templates</li>
<li>Manual but consistent handoff processes between data scientists and engineers</li>
</ul>
<h4>Stage 2: Basic Automation</h4>
Characteristics:
<ul>
<li>Simple deployment pipelines</li>
<li>Basic model versioning</li>
<li>Scheduled retraining processes</li>
<li>Initial monitoring tools</li>
</ul>
Business Impact:
<ul>
<li>Reduced deployment time (days to weeks)</li>
<li>Improved reproducibility</li>
<li>Lower operational friction</li>
<li>Faster issue detection</li>
</ul>
Implementation Approach:
Focus on automating the highest-friction processes:
<ul>
<li>Model packaging standards</li>
<li>Basic CI/CD integration</li>
<li>Scheduled performance dashboards</li>
<li>Automated testing frameworks</li>
</ul>
<h4>Stage 3: Advanced Automation</h4>
Characteristics:
<ul>
<li>Fully automated CI/CD pipelines</li>
<li>Comprehensive model registry</li>
<li>Automated data and model drift detection</li>
<li>A/B testing infrastructure</li>
</ul>
Business Impact:
<ul>
<li>Rapid deployment cycles (hours to days)</li>
<li>Complete reproducibility</li>
<li>Proactive issue prevention</li>
<li>Data-driven model updates</li>
</ul>
Implementation Approach:
Integrate more sophisticated tooling:
<ul>
<li>Feature stores for consistent feature engineering</li>
<li>Automated drift detection and alerting</li>
<li>Shadow deployment capabilities</li>
<li>Comprehensive metadata management</li>
</ul>
<h4>Stage 4: Autonomous Operations</h4>
Characteristics:
<ul>
<li>Self-healing ML pipelines</li>
<li>Automated model selection and optimization</li>
<li>Intelligent resource management</li>
<li>End-to-end observability</li>
</ul>
Business Impact:
<ul>
<li>Near-instantaneous deployments</li>
<li>Continuous optimization</li>
<li>Minimal operational overhead</li>
<li>Maximum business value capture</li>
</ul>
Implementation Approach:
Implement advanced capabilities for self-optimization:
<ul>
<li>Auto ML for continuous improvement</li>
<li>Automated incident response</li>
<li>Dynamic resource allocation</li>
<li>Comprehensive governance frameworks</li>
</ul>
<blockquote>
Most organizations begin at Stage 1 and should implement MLOps capabilities incrementally, focusing on the highest-value components for their specific business context rather than attempting a complete transformation overnight.
</blockquote>
<h2>Case Study: Transforming ML Deployment for a Digital Insurance Provider</h2>
A leading digital-native insurance provider approached CoffeeBeans with a critical challenge: their model deployment process was taking 45-60 days, significantly hindering their ability to update their hurricane prediction and risk assessment systems. This lengthy deployment cycle was directly impacting their market competitiveness and risk management capabilities.
Key Challenges:
<ul>
<li>Complex compliance requirements in the insurance industry</li>
<li>Multiple models requiring coordination</li>
<li>Limited model operationalization capabilities</li>
<li>Need for automated monitoring and AWS integration</li>
</ul>
Our Approach:
After conducting a comprehensive MLOps readiness assessment, we identified that the organization was at Stage 1 (Manual Operations) of our MLOps maturity model. We implemented a phased approach:
<ol>
<li>Foundation Building (Weeks 1-4)
<ul>
<li>Conducted a technical evaluation of platforms (AWS SageMaker, Databricks, MLflow)</li>
<li>Established standardized model packaging protocols</li>
<li>Created comprehensive documentation templates</li>
<li>Implemented a central model registry</li>
</ul>
</li>
<li>Automation Implementation (Weeks 5-10)
<ul>
<li>Developed automated testing frameworks for models</li>
<li>Created CI/CD pipelines for model deployment</li>
<li>Implemented basic model monitoring</li>
<li>Established audit trail and governance features</li>
</ul>
</li>
<li>Integration and Optimization (Weeks 11-14)
<ul>
<li>Connected MLOps pipelines with existing AWS infrastructure</li>
<li>Established automated approval workflows</li>
<li>Implemented comprehensive drift detection</li>
<li>Created executive dashboards for model performance</li>
</ul>
</li>
</ol>
Results:
Within 14 weeks, the organization transformed its ML operations capability:
<ul>
<li>Deployment time reduced from 45-60 days to same-day deployments</li>
<li>Regulatory compliance documentation automation saved 120+ person-hours per quarter</li>
<li>Model performance issues identified 83% faster</li>
<li>Overall risk exposure reduced by 27% through more frequent model updates</li>
</ul>
The business impact extended beyond operational improvements—the company launched two new insurance products ahead of competitors by leveraging their newfound ability to rapidly deploy and iterate on AI models.
<h2>Practical MLOps Implementation for Small and Medium Businesses</h2>
While comprehensive MLOps might seem overwhelming for smaller organizations, our experience has shown that even modest implementations can deliver significant value. Here's our recommendation for resource-constrained teams:
Focus on the "MLOps Essentials" first:
<ol>
<li>Version Control Everything
<ul>
<li>Use Git for code, data schemas, and configuration</li>
<li>Document experiment parameters and results</li>
<li>Store model artifacts with version information</li>
</ul>
</li>
<li>Create a Simple Deployment Pipeline
<ul>
<li>Standardize model packaging (e.g., Docker containers)</li>
<li>Implement basic testing before deployment</li>
<li>Create consistent deployment procedures</li>
</ul>
</li>
<li>Implement Basic Monitoring
<ul>
<li>Track model input/output distributions</li>
<li>Monitor prediction volumes and performance metrics</li>
<li>Set up simple alerting for critical thresholds</li>
</ul>
</li>
<li>Establish Governance Foundations
<ul>
<li>Document model details and training procedures</li>
<li>Create simple approval workflows</li>
<li>Maintain an inventory of deployed models</li>
</ul>
</li>
</ol>
For smaller organizations, this "MLOps Essentials" approach can be implemented in 6-8 weeks and typically requires just 1-2 dedicated resources with support from existing development teams. The ROI on this investment is consistently high, with organizations reporting 3-5x returns through reduced maintenance costs, improved model performance, and faster deployment cycles.
<h2>Strategic Recommendations</h2>
<img src="https://media.coffeebeans.io/Screenshot%202025-04-25%20at%206.41.45%C3%A2%C2%80%C2%AFPM.png" alt="">
To successfully implement MLOps in your organization, consider these key strategic actions:
<ul>
<li>Assess Your Current State: Evaluate your organization against the four stages of MLOps maturity to identify critical gaps.</li>
<li>Start Small, Scale Gradually: Begin with a single high-value ML use case and implement basic MLOps practices before expanding.</li>
<li>Prioritize Business Outcomes: Focus MLOps investments on capabilities that directly enable specific business value (e.g., faster time-to-market, improved model accuracy).</li>
<li>Build Cross-Functional Teams: Successful MLOps requires collaboration between data scientists, engineers, and business stakeholders—create team structures that facilitate this interaction.</li>
<li>Leverage Managed Services: Platforms like AWS SageMaker, Databricks, and specialized MLOps tools can accelerate implementation for resource-constrained teams.</li>
</ul>
<h2>Conclusion</h2>
As we've demonstrated through our <a href="https://coffeebeans.io/blogs/the-ai-readiness-continuum%C2%A9-where-does-your-organization-stand" title="AI Readiness Continumm">CoffeeBeans AI Readiness Continuum© </a>and <a href="https://coffeebeans.io/blogs/data-source-mapping-the-critical-first-step-in-your-ai-journey" title="Data Maturity Mapping">Data Source Mapping frameworks</a> in previous articles, becoming AI-ready is a journey that requires strategic investments in foundational capabilities. MLOps represents a critical bridge between AI experimentation and sustainable business value—the key to transforming promising prototypes into production systems that deliver measurable ROI.
By implementing appropriate MLOps practices for your organization's size and AI maturity level, you can dramatically accelerate your journey from AI concepts to business impact. The key is starting with the right scope, focusing on business outcomes, and building capabilities incrementally rather than attempting a complete transformation overnight.

How organizations can implement practical MLOps frameworks that reduce deployment time by 78%, lower maintenance costs by 45%, and dramatically improve model performance in production environments—regardless of company size or AI maturity level.

Operationalizing AI: The MLOps Advantage

By Prashanth Jayakumar &#x26; Siddharth Pratyush
<h2>Executive Summary</h2>
Organizations investing in artificial intelligence often focus prematurely on advanced algorithms while neglecting the essential foundation: understanding their data landscape. At CoffeeBeans, our extensive work with organizations across diverse industries has shown that
<blockquote>
83% of AI implementation challenges stem from inadequate data foundations,
</blockquote>
with incomplete data source mapping as the primary barrier. This article outlines our systematic Data Visibility Framework™ that has enabled our clients to reduce AI implementation timelines by 40% and significantly improve project success rates.
<h2>The Hidden Foundation of AI Success</h2>
The journey to AI maturity begins with a clear understanding of your organization's data ecosystem. As established in CoffeeBeans <a href="https://coffeebeans.io/blogs/the-ai-readiness-continuum%C2%A9-where-does-your-organization-stand" title="AI Readiness Continuum">AI Readiness Continuum© framework</a>, organizations at the Nascent stage typically operate with siloed data environments and limited integration capabilities—fundamental barriers to AI implementation.
Through our extensive experience implementing data and AI projects across multiple industries, the CoffeeBeans team has identified a direct correlation between comprehensive data source mapping and project success rates:
<ul>
<li>Organizations with incomplete data source maps experienced a 62% failure rate in AI initiatives</li>
<li>Companies that invested in thorough data source mapping achieved 3.4x higher ROI on AI investments</li>
<li>Well-executed mapping reduced implementation timelines by an average of 11.5 weeks</li>
</ul>
These findings underscore our core philosophy: before organizations can effectively leverage AI, they must first make their data visible, accessible, and integration-ready.
<h2>The Enterprise Data Complexity Challenge</h2>
Most organizations significantly underestimate the complexity of their data landscape. A mid-sized organization typically manages data across:
<ul>
<li>15-20 operational systems</li>
<li>8-12 departmental data repositories</li>
<li>5-7 cloud services with distinct data models</li>
<li>3-5 external data sources with varying formats</li>
<li>Numerous spreadsheets and unstructured documents</li>
</ul>
Without systematic mapping, organizations attempt AI implementations with an incomplete understanding of available data, leading to suboptimal models and missed opportunities.
Our research identifies four prevalent data integration challenges that proper mapping helps address:
<ol>
<li>Unknown data duplication: The same data elements exist in multiple systems with different update cycles, creating conflicting "sources of truth"</li>
<li>Missing relationship context: Critical relationships between data elements across systems remain undocumented</li>
<li>Quality inconsistency: Varying data quality standards across systems undermine reliable AI outputs</li>
<li>Hidden data gaps: Critical information needed for meaningful AI applications exists but remains undiscovered</li>
</ol>
<h2>A Systematic Approach to Data Source Mapping</h2>
At CoffeeBeans, we've developed the Data Visibility Framework™ – a structured methodology that brings clarity to your entire data ecosystem. This approach has been refined through our work with organizations of various sizes and is specifically designed for companies looking to accelerate their AI readiness journey:
<h4>Phase 1: Discovery and Inventory</h4>
Our team works with both IT and business stakeholders to create a comprehensive inventory of all data repositories, including operational systems, shadow IT, external data sources, and data flows between systems.
<h4>Phase 2: Data Element Analysis</h4>
Using our proprietary Data Element Catalog™ approach, we document key business entities, their attributes, metadata, and business context across all systems.
<h4>Phase 3: Relationship Mapping</h4>
Our data architects map how data elements relate across systems, identify cross-system entity resolution, and document data lineage using visual mapping techniques that make complex relationships comprehensible.
<h4>Phase 4: Quality and Governance Assessment</h4>
The CoffeeBeans Data Quality Scorecard™ provides a quantitative evaluation of data completeness, accuracy, consistency, and existing governance controls.
<h4>Phase 5: AI-Readiness Mapping</h4>
We connect your data landscape to potential AI use cases, identify gaps, and create an integration roadmap that leverages your existing technology investments, including platforms like Databricks, Snowflake, and DataOS.
<h2>Case Study: Manufacturing Excellence Through Data Visibility</h2>
A mid-sized manufacturer with $175M in annual revenue sought to implement predictive maintenance capabilities across their production facilities. Their initial AI proof-of-concept failed despite significant investment in advanced algorithms.
The CoffeeBeans team conducted an AI Readiness Assessment and identified critical data visibility challenges:
Key Challenges Identified:
<ul>
<li>Equipment performance data existed in three separate systems with no integration</li>
<li>Maintenance records were primarily paper-based with minimal digitization</li>
<li>Production scheduling data was disconnected from equipment utilization tracking</li>
<li>Sensor data was being collected but not systematically stored or analyzed</li>
</ul>
Using our Data Visibility Framework™, we helped the manufacturer implement our recommended foundation-first approach:
<ul>
<li>Created a unified equipment master data repository using Snowflake</li>
<li>Digitized and standardized maintenance records using our rapid digitization methodology</li>
<li>Implemented automated data integration between production scheduling and equipment monitoring</li>
<li>Established data quality standards and monitoring processes using CoffeeBeans Data Quality Protocols™</li>
</ul>
Results Within Six Months:
<ul>
<li>Predictive maintenance model accuracy improved from 61% to 89%</li>
<li>Unplanned downtime decreased by 37%</li>
<li>Maintenance costs reduced by 23%</li>
<li>ROI from the AI implementation exceeded initial projections by 2.8x</li>
</ul>
This engagement demonstrates how our thorough data source mapping approach transformed a struggling AI initiative into a significant business success—not by changing the AI algorithm, but by building the necessary data foundation that makes AI possible.
<h2>Industry-Specific Data Mapping Considerations</h2>
Data source mapping requirements vary significantly by industry:
<ol>
<li>Manufacturing
<ul>
<li>Priority Sources: ERP and MES systems, equipment sensors, maintenance systems</li>
<li>Key Challenges: OT/IT integration, legacy systems, inconsistent sensor data collection</li>
</ul>
</li>
<li>Financial Services
<ul>
<li>Priority Sources: Core banking systems, CRM, risk management platforms</li>
<li>Key Challenges: Siloed data across business lines, legacy systems, regulatory requirements</li>
</ul>
</li>
<li>Healthcare Tech
<ul>
<li>Priority Sources: EHR systems, clinical databases, medical device data</li>
<li>Key Challenges: Privacy regulations, interoperability issues, unstructured clinical data</li>
</ul>
</li>
<li>Retail/Ecommerce
<ul>
<li>Priority Sources: POS systems, e-commerce platforms, inventory management</li>
<li>Key Challenges: Omnichannel data fragmentation, product information inconsistency</li>
</ul>
</li>
</ol>
<h2>The Data Source Mapping Quick-Start Guide</h2>
For organizations ready to begin their data source mapping journey, we've developed a simplified 3-week approach based on our enterprise implementation experience:
<h4>Week 1: Rapid Discovery</h4>
The CoffeeBeans team facilitates system inventory workshops, documents data repositories, identifies priority business entities, and creates an initial system landscape diagram using our visual mapping tools.
<h4>Week 2: Priority Entity Mapping</h4>
Together with your team, we select 3-5 high-priority business entities, document where they exist across systems, identify key attributes, and map relationships using our Entity Relationship Accelerator™.
<h4>Week 3: Gap Analysis and Roadmapping</h4>
Our specialists assess data quality for priority entities, identify critical integration points, document governance gaps, and create a phased improvement roadmap that aligns with your business objectives and technology landscape.
This accelerated approach delivers immediate value while laying the groundwork for more comprehensive mapping. Unlike generic consulting engagements, our team brings practical implementation expertise with platforms like Databricks, Snowflake, and DataOS to ensure your roadmap is both ambitious and achievable.
<h4>Measuring Data Mapping Effectiveness</h4>
Track these key metrics to evaluate your data source mapping initiative:
<ul>
<li>Discovery effectiveness: Percentage of organizational data sources identified</li>
<li>Entity coverage: Percentage of key business entities fully mapped</li>
<li>Relationship documentation: Percentage of entity relationships documented</li>
<li>Quality transparency: Percentage of data elements with quality metrics</li>
<li>Governance clarity: Percentage of data elements with clear ownership</li>
<li>Business alignment: Number of potential AI use cases linked to available data</li>
</ul>
<h2>The Foundation-First Path to AI Success</h2>
As organizations progress along the CoffeeBeans <a href="https://coffeebeans.io/blogs/the-ai-readiness-continuum%C2%A9-where-does-your-organization-stand" title="AI Readiness Continuum">AI Readiness Continuum©</a>, data source mapping provides the essential foundation upon which all subsequent AI capabilities are built. At CoffeeBeans, our end-to-end approach—seamlessly integrating strategy and implementation—consistently demonstrates that investments in data visibility and integration readiness yield significantly higher returns than premature algorithm development.
What makes our approach unique is that we don't just provide strategy or implementation in isolation. Our teams work across the entire data-to-AI lifecycle, ensuring that the roadmap we create together can be executed effectively. This integrated methodology has proven particularly valuable for small and medium-sized businesses looking to accelerate their AI journey without the resources of larger enterprises.
By systematically mapping your data landscape, you not only enable more successful AI implementations but also create business value through improved data quality, enhanced decision-making, and operational efficiency—regardless of your AI ambitions.
The journey to becoming an AI-ready organization begins with this fundamental step: knowing what data you have, where it resides, how it connects, and what it can tell you. Let CoffeeBeans be your partner in this critical first step toward AI success.
Ready to map your path to AI success? Schedule a Data Foundation consultation with our expert team.

The journey to AI maturity begins with a clear understanding of your organization's data ecosystem

Data Source Mapping: The Critical First Step in Your AI Journey

<h6>By Prashanth Jayakumar, Siddharth Pratyush</h6>
In today's rapidly evolving business landscape, artificial intelligence (AI) has transitioned from a futuristic concept to a critical business imperative for organizations of all sizes. Yet, despite growing investments in AI technologies, a startling reality persists: only 15% of AI initiatives achieve their objectives. This sobering statistic raises a fundamental question for business leaders: Is your organization truly ready for AI implementation?
<h2>Understanding the AI Implementation Challenge</h2>
The promise of AI is compelling—enhanced operational efficiency, improved customer experience, new revenue streams, and competitive advantage. However, the journey from AI aspiration to value realization is fraught with challenges:
<ul>
<li>70% of companies lack the proper foundation for AI success</li>
<li>80% report significant challenges in data integration</li>
<li>65% struggle with identifying the right use cases</li>
</ul>
These statistics reveal a critical insight: AI implementation failure rarely stems from the technology itself but rather from organizational unpreparedness. Companies eager to embrace AI often leap into implementation without first establishing the necessary foundations—akin to building a skyscraper on unstable ground.
Small and medium-sized businesses face additional challenges, including limited technical expertise and resource constraints. Yet they also stand to gain significant competitive advantages through strategic AI adoption.
<h2>The AI Readiness Continuum©: A Framework for Assessment</h2>
The Coffeebeans' AI Readiness Continuum© is a framework developed through extensive work with organizations across diverse industries. This assessment methodology helps organizations identify their current position and chart a strategic path toward AI maturity across four distinct stages:
<img src="https://media.coffeebeans.io/Readiness.png" alt="">
<h4>Stage 1: Nascent</h4>
<img src="https://media.coffeebeans.io/Screenshot%202025-04-08%20at%207.14.06%C3%A2%C2%80%C2%AFPM.png" alt="">
<h4>Stage 2: Emerging</h4>
<img src="https://media.coffeebeans.io/Screenshot%202025-04-08%20at%207.14.14%C3%A2%C2%80%C2%AFPM.png" alt="">
<h4>Stage 3: Advanced</h4>
<img src="https://media.coffeebeans.io/Screenshot%202025-04-08%20at%207.14.23%C3%A2%C2%80%C2%AFPM.png" alt="">
<h4>Stage 4: Transformative</h4>
<img src="https://media.coffeebeans.io/Screenshot%202025-04-08%20at%207.14.33%C3%A2%C2%80%C2%AFPM.png" alt="">
Organizations typically progress through these stages sequentially, though the pace of advancement varies based on industry, resources, and strategic priorities. Understanding your current position is the essential first step in developing an effective AI readiness strategy.
<h2>Case Study: From Data Challenges to Dynamic Pricing Optimization</h2>
Consider the journey of a CoffeeBeans client—a growing retail chain that struggled with optimizing pricing decisions across 400,000+ SKUs and millions of daily transactions:
Initial Assessment Results:
<ul>
<li>Positioned at the "Nascent" stage on the AI Readiness Continuum©</li>
<li>Absence of automated systems for price optimization</li>
<li>Reliance on manual, Excel-based pricing decisions</li>
<li>Complex processing of competitor pricing data</li>
<li>Need for price explainability and audit trails</li>
</ul>
The CoffeeBeans Approach: Following our assessment, we recommended a foundation-first strategy. Rather than immediately implementing complex AI algorithms, we focused on building robust data infrastructure:
<ol>
<li>Foundation Building: Developed a distributed ETL pipeline using Apache Spark for data integration</li>
<li>Data Standardization: Implemented data standardization across multiple sources</li>
<li>Core Technology: Created a product name disambiguation engine to ensure data accuracy</li>
<li>Rule Integration: Built a rule-based pricing computation system incorporating inventory levels, competitor pricing, and seasonal factors</li>
<li>Visualization Layer: Deployed a custom UI for visualization and control</li>
</ol>
Results:
<ul>
<li>Automated pricing decisions for 50,000+ products daily</li>
<li>Reduced pricing decision cycle time from 2-3 days to just 2-3 hours</li>
<li>Enabled overnight updates for next-day operations</li>
<li>12% improvement in margin performance within the first quarter</li>
<li>ROI achieved within 7 months of implementation</li>
</ul>
This case exemplifies how proper assessment using the AI Readiness Continuum© and strategic investment in data foundations can unlock transformative AI capabilities—even for organizations starting at the earliest stages of readiness.
<h2>Industry-Specific Readiness Considerations</h2>
AI readiness requirements vary significantly across industries:
<img src="https://media.coffeebeans.io/Screenshot%202025-04-08%20at%207.33.09%C3%A2%C2%80%C2%AFPM.png" alt="">
These industry-specific considerations should inform your AI readiness strategy, ensuring that your approach aligns with the unique challenges and opportunities in your sector.
<h2>Strategic Recommendations for Advancing Your AI Readiness</h2>
Based on an organization's current position on the AI Readiness Continuum©, different strategic priorities emerge:
For Organizations at the Nascent Stage:
<ol>
<li>Conduct a comprehensive data maturity assessment to identify critical gaps and priorities</li>
<li>Establish a unified data strategy aligned with business objectives</li>
<li>Invest in foundational data infrastructure to consolidate siloed data sources</li>
<li>Implement basic data governance frameworks to ensure data quality and consistency</li>
<li>Develop data literacy programs to build organizational capabilities</li>
</ol>
<h4>For Organizations at the Emerging Stage:</h4>
<ol>
<li>Enhance data architecture to support advanced analytics and AI workloads</li>
<li>Implement automated data pipelines to improve data freshness and reliability</li>
<li>Develop a formalized AI use case identification process aligned with business priorities</li>
<li>Establish cross-functional teams to bridge technical and business perspectives</li>
<li>Pilot AI initiatives in areas with clear ROI potential and measurable outcomes</li>
</ol>
<h4>For Organizations at the Advanced Stage:</h4>
<ol>
<li>Implement MLOps practices to streamline model development and deployment</li>
<li>Scale successful AI initiatives across the organization</li>
<li>Establish AI centers of excellence to share best practices and accelerate innovation</li>
<li>Develop comprehensive AI governance frameworks addressing ethics, bias, and transparency</li>
<li>Create feedback mechanisms to continuously improve AI performance</li>
</ol>
<h4>For Organizations at the Transformative Stage:</h4>
<ol>
<li>Explore cutting-edge AI applications including generative and agentic AI</li>
<li>Develop ecosystem partnerships to extend AI capabilities</li>
<li>Create AI-driven products and services as new revenue streams</li>
<li>Implement continuous learning systems that adapt to changing conditions</li>
<li>Lead industry standards and practices for responsible AI</li>
</ol>
These recommendations provide a roadmap for progressive advancement along the continuum, enabling organizations to build on their current capabilities while preparing for the next stage of AI maturity.
<h2>The Foundation-First Approach to AI Success</h2>
Experience across industries has consistently shown that a methodical, foundation-first approach to AI readiness yields significantly better outcomes than rushing to implement the latest AI technologies without proper preparation. Organizations that methodically advance along the AI Readiness Continuum© gain significant competitive advantages:
<ul>
<li>Speed to Value: Reduced time from concept to implementation of AI solutions</li>
<li>Resource Efficiency: More effective allocation of technology and talent investments</li>
<li>Risk Reduction: Lower failure rates for AI initiatives</li>
<li>Scalability: Ability to rapidly scale successful AI applications across the organization</li>
<li>Market Responsiveness: Enhanced capacity to adapt to changing market conditions</li>
</ul>
In an era where AI capabilities increasingly differentiate market leaders from laggards, understanding and systematically improving your organization's position on the AI Readiness Continuum© isn't just a technology consideration—it's a strategic imperative for sustainable growth.
<h2>Looking Ahead: The Evolving AI Landscape</h2>
As AI technologies continue to evolve at an unprecedented pace, the definition of "readiness" will also evolve. Organizations must build not only for current AI capabilities but also establish foundations flexible enough to accommodate emerging technologies and approaches:
<ul>
<li>Generative AI capabilities that create new content, designs, and solutions</li>
<li>Agentic AI systems that autonomously accomplish complex tasks</li>
<li>Edge AI that enables real-time processing at the point of action</li>
<li>Collaborative AI that enhances human-machine partnerships</li>
</ul>
The most resilient organizations will develop "readiness flexibility"—the capacity to quickly adapt their data and AI foundations to harness new technological capabilities as they emerge.
<h2>Take the Next Step in Your AI Readiness Journey</h2>
Understanding where your organization stands on the AI Readiness Continuum© is the crucial first step toward realizing the full potential of AI. By addressing fundamental readiness gaps before rushing into implementation, you can dramatically increase your likelihood of AI success.

In our latest thought leadership piece, we explore the AI Readiness Continuum© - a framework for understanding where your organization stands on the path to AI maturity.

The AI Readiness Continuum©: Where Does Your Organization Stand?

The data maturity of an organization is a measure of how well that organization leverages data to make business decisions. The higher the <a href="https://coffeebeans.io/blogs/how-to-assess-your-organization&#x27;s-ai-readiness-a-comprehensive-guide?utm_source=Website%20blog&#x26;utm_medium=Website%20blog&#x26;utm_campaign=Website%20blog">data</a> maturity level the more likely the organization is to achieve financial success and enhanced reputation. As per the Harvard Business Review only 1 in 4 companies have reached the final stage of data maturity. Also, 91% of enterprises have not reached a “transformational” level, where they utilize data to predict and create a desired future.  In order to know the data maturity of an organization it is necessary to conduct a data maturity assessment. In this blog post your organization will learn the concept of data maturity, the process of conducting a quality data maturity assessment, the different stages of data maturity, benefits of assessing data maturity, challenges in building data maturity as well as how to increase data maturity levels in your organization.
<img src="https://media.coffeebeans.io/s.png" alt="">
<h1>Introduction to Data Maturity</h1>
Data maturity informs us in what stage a particular organization stands as regards to the gathering, analysis, and utilization of data. Organizations such as Amazon, Airbnb, Spotify, and Netflix utilize data to comprehend clients and craft products and services serving their unique and specific requirements. Not all entities have the same levels of data maturity. A data maturity assessment makes an entity aware of how advanced it is managing as well as utilizing data.  Entities that skillfully employ data are able to overtake the competition, have more innovative deliverables as well as take superior data-driven decisions. Note that achieving desired data maturity in any organization entails careful planning and takes time. According to HPE Data, the average level of data maturity in enterprises is 2.6 out of 5. This means that many are still in the early stages of fully comprehending as well as utilizing their data.
<h2>How to Conduct a Data Maturity Assessment</h2>
At the beginning, outline the data assets to be assessed. Next find the person(s) responsible for analyzing the data. The individual(s) who view the reports generated from the data need to be identified. Also find out the person(s) having ownership of that data. Carry out a survey of the people in the organization responsible for data management. Compare various data assets on the basis of their results. Find out the reasons why certain data results have poor results. Next, utilize the results to take relevant action. Craft a detailed data strategy which gives more emphasis on poorly performing data assets. A relevant question would be whether the existing tools and processes in your organization  are apt and suitable. Ask how your organization measures the success of projects. Find out how your organization carries out the collection, storage, and analysis of data. Are data and discoveries shared across the organization? Do employees have access to data and analytics tools?
There are various Data Maturity Assessment models. Each is meant to find out the data maturity level of respective organizations. Few of them are DAMA Data Management Maturity Model (DMM), Gartner’s Enterprise Information Management (EIM) Maturity Model, CMMI Data Maturity Model, TDWI Data Maturity Model, AI and Data Maturity Model by McKinsey and IBM Data Governance Maturity Model.
<h2>The Different Stages of Data Maturity</h2>
<ul>
<li>Data-exploring</li>
</ul>
Organizations realize the necessity to gather data. However, they are not utilizing the full potential of the data. Decisions are mostly taken with limited or no data. There are no best practices for data management. Mostly the data exists in siloes. Leaders make decisions based on their experience or instinct. This is certainly not a scientific or logical way to make decisions. Most employees are unaware of what data exists or how to utilize them.
<ul>
<li>Data-informed</li>
</ul>
The organization's leadership is starting to show interest in investing in data-related technologies as well as in how data-driven applications can help the organization. Teams get to utilize self-serve applications that give solutions to questions in minimum time. Organizations realize the vital role of data. However, in practice they seldom utilize data for making strategic decisions.
<ul>
<li>Data-driven</li>
</ul>
In this stage there is democratization of data. The decision making is data-driven. Vetted and complete data is available for use. Investment leverages past experiences for maximum business impact. KPI (Key Performance Indicators) as well as metrics for successful outcomes are established. These metrics are monitored for evaluation purposes.
<ul>
<li>Data-transformed</li>
</ul>
Data is part of all aspects of your organization. It is a practice to share data in real-time all across the organization. Freshers are made data literate from the onset. Investment is made in data literacy as well as in data maintenance. Leaders are committed to data-driven decisions. Employees are aware of their roles as well as responsibilities. Here, leaders and data teams are aligned.
<h2>Benefits of Assessing Data Maturity</h2>
Organizations remain ahead of competitors by harnessing data for superior decisions. Superior action is taken with respect to investing in technology as well as data governance. Data strategy is aligned with the short-term as well as long-term goals of the organization. Strengths and weaknesses are found out. This measure helps target improvement in the right areas.
<h2>Challenges in Building Data Maturity</h2>
Transitioning from a limited data management entity to a full-fledged data-driven enterprise requires overcoming many obstacles. We take a look at certain frequent challenges confronted by enterprises moving up the data maturity ladder.
<ul>
<li>Scalability</li>
</ul>
As the amount of data surges, several entities discover that their present infrastructure lacks the scalability to accommodate demands. It is imperative to make sure that data infrastructure is able to deal with higher volume data storage as well as processing.
<ul>
<li>Governance</li>
</ul>
Establishing data governance frameworks ensures superior data management as well as compliance with existing regulations. This measure can be challenging given the rigorous regulations which are different in different geographies as well as domains.
<ul>
<li>Cultural Adjustment</li>
</ul>
Achieving a data-driven culture involves getting rid of old mindsets and enhancing data literacy. This process usually takes time and is perhaps met with resistance or reluctance.
<ul>
<li>Data Quality Concerns</li>
</ul>
Since data is usually extracted from heterogeneous sources, the obtained data may not be adequately consistent or accurate. High levels of quality demand superior ETL (Extract Load Transform) procedures as well as uninterrupted validation of data.
<ul>
<li>Budgetary Constraints</li>
</ul>
Obtaining superior data maturity standards demands spending on data professionals, technologies as well as organizational processes. Smaller organizations and those having small budgets may struggle or fail to make the required investments.
<h2>How to Ramp Up Data Maturity Within Your Enterprise?</h2>
<ul>
<li>Invest in the Right Technologies for Obtaining Desired Results</li>
</ul>
Technology applications should be utilized for the storage and querying of the organization's data. The latest technologies should be leveraged in machine learning to operate advanced analytical models. It is necessary that data flowing through data pipelines are clean as well as reliable. Spending in a quality data integration application is necessary to make sure data from the organization's sources are consistent as well as standardized.
<ul>
<li>Leadership Involvement and Cultural Acceptance</li>
</ul>
The leadership should be united regarding the importance of data. There should be a CDO (Chief Data Officer) post created. The CDO should be responsible for making the whole organization data focused.
<ul>
<li>Investment in Training</li>
</ul>
Organizations are confronted with two barriers while implementing a data-driven culture. The barriers are lack of relevant skills and reluctance to change. Training programs must be conducted so that all staff are equipped with data-related skills. Also, counselling should be provided emphasizing the importance of data for individual and organizational success.
<ul>
<li>Employing Automation</li>
</ul>
The first step to achieve digital maturity is selecting a suitable data integration platform. Automating the data integration procedure frees the data team from devoting time on time intensive as well as repetitive manual activities.
<ul>
<li>Invest Right in both Purchasing Infrastructure and Training Human Resources</li>
</ul>
If your organization invests only in the technology and not spending on training staff it will result in having infrastructure that the staff are not able to utilize satisfactorily. On the other hand, a well-trained staff lacking the requisite infrastructure won’t be able to accomplish much with existing data.
Other steps include ramping up analytics capabilities. Advance from basic reporting in your organization to predictive as well as prescriptive analytics.
<h2>How to go about Conducting Data Maturity Assessment for your Organization</h2>
Now that you have learnt about the importance of data maturity assessment it's time to carry out a data maturity assessment for your organization. If you have the in-house capability then do the assessment at the earliest. In the event you lack the in-house capability consider outsourcing the work to a reputed firm specializing in data maturity assessment. Do research and come up with a list of firms. Do due diligence on each firm. Ask for references from the respective firms. Talk with past clients to evaluate the level of performance and reliability. Do not make the mistake of hiring the first competent firm you come across. Have a list of questions ready to ask each firm. Finally, discuss with the stakeholders of your organization before making a final decision.
With the relevant expertise and experience in data maturity assessment, <a href="https://coffeebeans.io/">CoffeeBeans </a> is well positioned to meet your organization’s goals as well as objectives. We have a pool of talented professionals proficient in data maturity assessment. They will assess your organization’s data maturity quickly and with precision. Our clients can vouch for our transparency, real-time response as well as adherence to stipulated deadlines. We provide stellar quality at competitive rates. Reach out to us at <a href="mailto:enquiries@coffeebeans.io">enquiries@coffeebeans.io</a> to know how we can help your organization obtain its unique and specific needs as well as preferences.

The data maturity of an organization is a measure of how well that organization leverages data to make business decisions. The higher the data maturity level the more likely the organization is to achieve financial success and enhanced reputation.

Data-Driven Growth: Why a Data Maturity Assessment Is Your First Step

Presently many of us are of the opinion that data is the new oil. However, available data needs to be skillfully collected, organized as well as leveraged for maximum benefit.  For your information, Databricks and Snowflake are two of the prominent <a href="https://coffeebeans.io/blogs/from-data-silos-to-ai-excellence-your-roadmap-to-readiness?utm_source=website%20blog&#x26;utm_medium=website%20blog&#x26;utm_campaign=website%20blog">data</a> management platforms.  Databricks' revenue for the fiscal year ending January 31, 2024 was over $1.6 billion, which is a 50% year-over-year increase.  Snowflake's revenue for the financial year ending January 31, 2024 was over $2.8 billion, which is up from almost $2.1 billion the previous year.  In this blog post, we learn what Databricks and Snowflake are, their respective benefits as well as when to utilize the two popular data management platforms.
<img src="https://media.coffeebeans.io/Website-1.jpg" alt="">
<h1>What is Databricks?</h1>
Databricks includes the novel Lakehouse architecture that contains the best aspects of data lake as well as data warehouse. The former utilizes Apache Spark. For your information Spark is the dominant big data processing tool. Databricks is strong in advanced analytics as well as machine learning. Many organizations leverage Databricks for real-time data processing. The architecture of Databricks is more complex than Snowflake’s. Did you know that Databricks is able to process data around twelve times faster when compared to other data management platforms? Databricks comes with functionality including integrated development environments as well as version control enabling different data professionals to work together on projects. The platform is designed as a PaaS (Platform as a Service). 
Databricks has the seamless scalability of data lakes as well as the superior performance of data warehouses. The platform handles AI-related workloads at competitive rates. Founded in 2013, Databricks facilitates data professionals to collaborate efficiently as well as effectively. The platform provides support for different coding languages including R, SQL, and Python. Databricks can handle structured, semi-structured or unstructured data in an efficient as well as effective way. Note that Databricks is excellent for applications such as recommendation engines as well as predictive analytics. The platform has the facility for custom configurations as well as optimizations. This platform has inbuilt machine learning libraries. There exists support for powerful ML frameworks including TensorFlow and PyTorch. Databricks integrates seamlessly with prominent BI tools including Tableau as well as Looker.
<h1>Advantages of Databricks</h1>
<ul>
<li>No Responsibility of Data Storage</li>
</ul>
Databricks focuses on data processing. It does not need to do data storage. Data can be stored on cloud platforms or even on-premises. 
<ul>
<li>Comprehensive Documentation</li>
</ul>
The  documentation of Databricks is well detailed. Users can refer to it for resolving their doubts as well as queries. 
<ul>
<li>Active Community</li>
</ul>
Users of Databricks can approach the community for support. If they run into challenges or issues there will surely be somebody to help them out. 
<ul>
<li>Collaboration Functionality</li>
</ul>
There exist shared notebooks as well as dashboards in Databricks for users to collaborate effectively as well as efficiently.
<ul>
<li>Highly Secure</li>
</ul>
Databricks boasts of enterprise grade security which makes the platform resilient to threats such as cyberattacks and events such as data breaches. 
<ul>
<li>Superior Integration</li>
</ul>
Databricks can seamlessly integrate with open-source tools and technologies as well as do away with the problem of vendor lock-in. 
<ul>
<li>Single Platform</li>
</ul>
The platform has a single environment catering to activities such as machine learning, data engineering as well as data science activities.
<h1>What is Snowflake?</h1>
Snowflake is a leading <a href="https://coffeebeans.io/blogs/the-future-of-enterprise-cloud-technology-5-trends-to-watch-out-for-in-2024?utm_source=website%20blog&#x26;utm_medium=website%20blog&#x26;utm_campaign=website%20blog">cloud-based</a> data warehouse technology. The storage module and compute module are separated in Snowflake’s architecture. The technology operates as a SaaS (Software as a Service). Snowflake is highly popular among organizations belonging to the financial sector. The platform is known for its cloud-agnostic nature. Snowflake has a simple as well as predictable cost structure. The processing module is made up of virtual warehouses. The latter are independent compute clusters capable of scaling dynamically as per workload requirements. Note that the architecture of Snowflake is simpler than that of Databrick.
Snowflake is ideal for SQL based analytics offering superior performance for the same. The platform does data warehousing at competitive rates. Snowflake comes with a powerful SQL engine. For your information, the platform was founded in 2012. Snowflake includes role-based access control functionality for security purposes. Snowflake is ideal for organizations with data predominantly in organized formats. The popular platform boasts of automatic scaling as well as resource management.
<h1>Advantages of Snowflake</h1>
<ul>
<li>Faster Query Response</li>
</ul>
Thanks to intelligent caching, database queries have low latency. This means that users get results faster compared to other data platforms. 
<ul>
<li>User Friendly</li>
</ul>
Snowflake boasts of an intuitive UI. The former utilizes SQL which most users are familiar with.
<ul>
<li>Convenient Pricing Model</li>
</ul>
Users have to pay only for storage resources and compute resources they use. Note that storage and compute costs are billed separately. There is the option of scaling up or scaling down resource usage as per the requirements of your organization.
<ul>
<li>Time-travel Facility</li>
</ul>
If users wish they can revert to the older saved versions of the data.  This is a unique feature offered by Snowflake.
<ul>
<li>Micro-partitioning Feature</li>
</ul>
Data is partitioned into separate units. This enables faster and better performance of Snowflake. When retrieving data the presence of smaller units results in lower latency.
<ul>
<li>Superior Security and Compliance</li>
</ul>
Snowflake keeps data safe as well as secure. The former ensures compliance with regulations as per region as well as industry. This makes it a popular as well as superior choice for the data needs of different organizations. 
<ul>
<li>Robust Data Recovery</li>
</ul>
Multiple copies of the data are taken and stored in different locations. In case of a manmade or natural disaster the data is not lost. So, organizations utilizing Snowflake do not have business down time. This prevents monetary and reputational losses.
<h2>When to Utilize Databricks</h2>
<ul>
<li>If your organization has individuals with technical knowledge to fully leverage Databricks then Databricks is the right choice. Note that utilizing Databricks involves a substantial learning curve.</li>
<li>If there is raw data present Databricks is the better choice.</li>
<li>Utilize Databricks for complex data science as well as machine learning modules.</li>
<li>Utilize Databricks for research &#x26; development activities. The platform empowers researchers to change the environment as per respective evolving demands.</li>
<li>As you know, Databricks can accommodate a wide range of data types as well as formats. Utilize it for entities in retail, media, or sectors with diverse data sources.</li>
<li>Choose Databricks for machine learning as well as predictive analytics. The platform facilitates preparing data, training of machine learning models and other activities.</li>
<li>Choose Databricks for entities managing vast datasets mandating complex transformation as well as analysis. These could range from Internet of Things data processing to social media analytics.</li>
<li>Choose Databricks for AI-related demands as well as demand for real-time data processing.</li>
<li>When there is a requirement for multiple languages support.</li>
</ul>
<h2>When to Utilize Snowflake</h2>
<ul>
<li>If your organization has many members with limited or no technical knowledge then Snowflake is the better option. Note that Snowflake has a gentle learning curve. Your organization saves time, money as well as effort in training resources as well as programs.</li>
<li>Choose Snowflake for sectors where different departments as well as external partners require to tap specific datasets. For your information, Snowflake has superior data sharing functionality. </li>
<li>In scenarios involving concurrency Snowflake is the best choice.  The platform can handle a very high number of concurrent users.</li>
<li>When the data is well structured Snowflake provides real-time analytics. If your organization deals with well-structured data, Snowflake offers superior performance.</li>
<li>When there is demand for generating reports, useful dashboards as well as superior data visualization go for Snowflake. The latter has robust support for business intelligence as well as analytics.</li>
<li>If your organization’s priority is ease of use, Snowflake wins hands down.</li>
<li>Choose Snowflake for batch-processing demands</li>
</ul>
<h2>A Final Note</h2>
The best choice for organizations depends upon their respective long-term data strategies. Experiment with both platforms to learn which one is suited to your organization's future enterprise requirements. Note that it is possible to utilize both Databricks and Snowflake in your organization. This step allows organizations to benefit from the advantages of Databricks as well as Snowflake. Also, organizations can reap benefits from the synergy of utilizing Databricks as well as Snowflake. Keep in mind whereas Databricks and Snowflake have differences, there exists also certain overlapping of features. 
<h2>How to Go About Choosing between Databricks and Snowflake for Your Organization</h2>
If you have the in-house expertise utilize it to determine whether Databricks or Snowflake is the best fit for your organization’s needs. In the event you lack the capability, consider outsourcing the decision to a reputed firm. Do research to find out the firms offering this service. Conduct due diligence on each firm. Do not make the mistake of hiring the first competent firm you come across. Examine other firms and have a list of questions to ask. Brainstorm with the other stakeholders of your organization before making a final decision. 
With the relevant experience and expertise in Databricks and Snowflake, CoffeeBeans is well positioned to help your organization meet its goals and objectives. We have a pool of talented professionals proficient in Databricks as well as Snowflake. They will integrate Databricks or Snowflake with your organization’s existing systems as per your organization’s choice. Our clients can vouch for our transparency, real-time response as well as adherence to stipulated deadlines. We offer stellar quality at competitive rates. Reach out to us at  <a href="mailto:atenquiries@coffeebeans.io">enquiries@coffeebeans.io</a>  to know how we can help your organization obtain its unique and specific requirements as well as preferences.

Presently many of us are of the opinion that data is the new oil. However, available data needs to be skillfully collected, organized as well as leveraged for maximum benefit.

Databricks vs. Snowflake: Which One Fits Your Data Strategy?

Did you know that the global AI (Artificial Intelligence) market is valued at over $390 billion? The AI market is expanding at a CAGR of 37.3% between 2022 to 2030. More and more organizations are adopting AI to obtain its myriad benefits as well as stay relevant in the intensely competitive market. Your organization may be considering implementing AI across its systems, processes, and operations. The question is whether your organization is ready to do so. This not only involves having the necessary infrastructure, support from the leadership, acceptance from employees, required financial resources as well as compatibility with the latest AI tools and technologies. Implementing AI before evaluating <a href="https://coffeebeans.io/blogs/how-to-assess-your-organization&#x27;s-ai-readiness-a-comprehensive-guide?utm_source=Website%20blog&#x26;utm_medium=Website%20blog&#x26;utm_campaign=Website%20blog">AI readiness</a> of your organization can lead to failure as well as wastage of considerable time, money, and effort. Therefore, it is imperative to assess your organization’s AI readiness at the onset. In this blog post, organizations will learn what AI readiness means, the importance of AI readiness, how to evaluate AI readiness for an organization, challenges to attain AI readiness and the roadmap for AI readiness. 
<img src="https://media.coffeebeans.io/From-Data-Silos-to-AI-Excellence--Your-Roadmap-to-Readiness.jpg" alt="">
<h1>The Concept of AI Readiness</h1>
AI readiness is a measure of an entity's ability to implement as well as integrate AI technologies into its processes, systems as well as operations. This is achieved by examining different parts of an entity including existing hardware and software, the quality of and availability of data, human resources with AI skills as well as ease of integration. The more prepared a firm is, the easier it is to implement AI across the firm and come up with better and innovative products and services as well as ramped up productivity and revenue.
<h2>The Significance of AI Readiness</h2>
By carrying out AI readiness entities become aware of possible challenges and minimize the risks involving AI implementation. Organizations leveraging AI are superior to rivals as they have the capability to make superior as well as data-driven decisions. The technology supplies useful insights to ramp up present revenue sources as well as identify fresh revenue sources. By leveraging AI, organizations experience ramped up efficiency as well as productivity. Time is saved and expenses are slashed.
<h2>Some Interesting Statistics</h2>
In the coming 5 years, entities expect AI to be a game-changer. 90% expect AI to ramp up growth, while 86% expect it to boost productivity. A further 84% believe it will fuel innovation in their respective industries.
<h2>Different Phases in An AI Readiness Roadmap</h2>
<ul>
<li>Exploring</li>
</ul>
In this phase, businesses are just starting off with AI. The former may be experimenting with the technology in a few parts of the enterprise. The target is to obtain basic knowledge and assess possible benefits. Firms ought to focus on pinpointing few use cases matching their goals and targets. Another part of this phase is to put in place a rudimentary infrastructure to run AI initiatives.
<ul>
<li>Planning</li>
</ul>
The target is to get the leadership on board. This is necessary for budget approval. A strategy is crafted with goals clearly listed. Use cases are examined for feasibility as well as impact.
<ul>
<li>Implementing</li>
</ul>
This stage mandates technological readiness as well as a mindset open to change and continuous learning. There should be awareness of AI's myriad short-term as well as long-term benefits. The dataset to train AI models should be diverse as well as include real-time scenarios. Feedback is used to refine the model in order to generate superior outputs.
<ul>
<li>Scaling</li>
</ul>
AI solutions are continuously monitored to gauge whether the former are performing satisfactorily as well as possess ease of scaling. If needed, the solutions are optimized as well as modified. The infrastructure is changed to handle greater workload.
<ul>
<li>Realizing</li>
</ul>
This is the last stage of AI readiness. AI solutions have to be adjusted to leverage the latest AI technologies as well as changing market needs. Now organizations can quantify value addition as a result of adopting AI tools and technologies. Investments are made in advanced AI technology to retain a competitive edge in the fast-evolving market.
<h2>Evaluating AI Readiness</h2>
We look at how to evaluate AI readiness of your organization. This helps organizations know how well they are prepared to adopt AI successfully as well as effectively. 
<ul>
<li>Leadership and Employee Readiness</li>
</ul>
Go through the enterprise systems and processes to find out areas where implementing AI can lead to value addition. Find out the enthusiasm of the leadership of adopting AI. Are they aware of the benefits of implementing AI? Are they ready to fund the implementation that involves purchasing necessary hardware, software, and other infrastructure? Are they ready to spend money on training the employees in AI technology? Are the employees possessing the necessary AI skills? If not, is your organization willing to spend money and time to make sure its employees are up-to-date? Are your organization employees ready to learn new concepts and willing to transit to new roles? Self-learning through provided resources or training sessions conducted by AI experts will help employees become well-versed in AI technologies. Note that individuals should be eager to learn new AI concepts as well as mentally ready to continually learn. 
<ul>
<li>Data Readiness</li>
</ul>
Is your data high-quality, complete, accurate, relevant, and accessible. Consider using quality data cleansing tools to clean data. Does the data adhere to existing regulations such as GDPR (General Data Protection Regulation), HIPAA (Health Insurance Portability and Accountability Act), CCPA (California Consumer Privacy Act) and others? This is important in sensitive sectors such as finance and healthcare. Are there cybersecurity measures in place to protect the data from cyberattacks and data measures. Consider using multi-factor authentication as well as role-based access control to protect the privacy and integrity of data. Organize as well as update data periodically without fail to ensure that it remains valuable to the existing as well evolving AI infrastructure.
<ul>
<li>Infrastructure Readiness</li>
</ul>
AI requires high processing power and storage capabilities. Can your organization's hardware and software scale up to meet the demand? If you have legacy systems, consider upgrading them. Your organization may consider cloud-based services to ramp up both processing and storage capabilities. Identify AI tools and technologies to purchase and subsequently integrate them in your organization's processes. Evaluate available AI tools and technologies. Keeping in mind your organizations’ financial capabilities, unique needs and goals while purchasing the appropriate AI tools as well as technologies.  Flexibility is a desired aspect of AI operations. Make sure that your organization's existing processes as well as systems are truly flexible. 
For your information there are AI readiness assessment tools that help in pinpointing gaps as well as areas needing improvement. This helps organizations efficiently as well as effectively fix the issues.
<h2>More about AI Readiness</h2>
All AI activities should be on the same page as the business strategy. The former should enable desired results. Each AI application should have a useful contribution to your organization's long-term objectives. Invest in regular training so that the leadership and staff are aware of the latest AI advancements and incorporate them into the day-to-day operations of the organization. Investment in training and AI-ready infrastructure will surely yield a significant ROI (Return on Investment) in the long-term. It is extremely important that AI be utilized responsibly. A strong AI governance framework is needed to ensure that. AI models should not have biased results or result in discrimination. The solution to this problem is in evaluating the training data. The latter should be diverse and reflect the real world. This will build trust among your organization's clients as well as stakeholders.
<h2>Challenges to Attain AI Readiness</h2>
The route to AI readiness is not without hurdles. Organizations must work on a priority basis to overcome possible challenges. The latter are given below.
<ul>
<li>Privacy and Ethical Aspects</li>
</ul>
There are legitimate aspects related to the possible misuse of AI technology. Having a strong ethical system and complete regulatory compliance are needed to resolve these issues.
<ul>
<li>Lack of Awareness of AI Benefits</li>
</ul>
The people in your organization may have too high expectations or perceive AI adoption as risky. The technology may also seem unfamiliar to some leading to internal resistance within the organization.
<ul>
<li>Financial Challenges</li>
</ul>
Every organization cannot afford the latest AI tools as well as the needed budget to train all members of the organization.
<ul>
<li>Data Silos</li>
</ul>
The organization’s data may be stored in different departments. Each department may have knowledge of only the local data. The former may be unaware of the data stored in other departments. It may be a challenge to break down these data silos. 
<h2>How to go about Conducting AI readiness for your Organization</h2>
If your organization has the in-house expertise to conduct <a href="https://coffeebeans.io/consulting/ai?utm_source=website%20blog&#x26;utm_medium=website%20blog&#x26;utm_campaign=website%20blog">AI </a> readiness then do so at the earliest. Else consider hiring the services of a firm specializing in carrying out AI readiness. Ask for references from potential firms. Speak to past clients to evaluate the concerned firm’s performance and reliability. Conduct the needed due diligence. Do not make the mistake of hiring the first competent firm you come across. Evaluate multiple firms to have a range of choices. Finally consult your organization’s stakeholders before making the final decision of selecting a particular firm. Note that the process of AI readiness never ends. In practice, it involves continuous learning as well as adoption.
With the necessary expertise and experience in conducting AI readiness, CoffeeBeans is well positioned to help your organization meet its goals and objectives. We have a pool of professionals proficient in carrying out AI readiness. They will do the task systematically, efficiently as well as effectively. Our clients can vouch for our transparency, real-time response as well as adherence to stipulated deadlines.  We provide stellar quality at competitive rates. Reach out to us at <a href="mailto:enquiries@coffeebeans.io">enquiries@coffeebeans.io</a> to know how we can help your organization obtain its specific and unique needs and preferences.

Did you know that the global AI (Artificial Intelligence) market is valued at over $390 billion? The AI market is expanding at a CAGR of 37.3% between 2022 to 2030. More and more organizations are adopting AI to obtain its myriad benefits as well as stay relevant in the intensely competitive market.

From Data Silos to AI Excellence: Your Roadmap to Readiness

Organizations are increasingly adopting <a href="https://coffeebeans.io/primary-services/ai?utm_source=Website%20blog&#x26;utm_medium=Website%20blog&#x26;utm_campaign=Website%20blog">AI (Artificial Intelligence) </a>for the technology’s significant benefits and also to stay relevant in the intensely competitive market. But before implementation of AI across your organization you have to evaluate whether your organization has the necessary attributes for successful AI implementation. For your information, 77% of companies are utilizing or exploring AI, and 83% consider it a top priority. Also, 65% of organizations are regularly using generative AI, which is nearly double the amount in 2023. The global AI market is currently worth around $391 billion, which is an increase of around $195 billion since 2023. As many as 23% of customer service companies utilize AI. In healthcare, AI can automate up to 73% of administrative tasks. In this blog post, organizations will learn how to assess their current AI readiness.  These include employee skills, investment cost, technical feasibility, cultural factors, integration with existing systems, data quality as well as compliance with regulations. This way they can take the necessary steps and measures to make their organization completely ready for effective AI implementation.
<img src="https://media.coffeebeans.io/How-to-Assess-Your-Organization&#x27;s-AI-Readiness.jpg" alt="">
<h1>Why Should Organizations Utilize AI?</h1>
AI has the capability to analyze very large amounts of data which is a feature that cannot be matched by humans. As a result, the technology pinpoints trends as well as supplies valuable insights previously missed by the human eye. Benefits of AI include ramped up productivity, increased innovation, solution to complex problems, superior client service, automation of previously manual processes and raised operational efficiency among others. So, organizations stand to gain a lot by implementing AI in their processes, operations as well as systems. An important advantage of AI adoption is effective analysis of client behavior as well as operations. As a result, enterprises can offer personalized products as well as services to existing and potential clients. Note that AI adoption involves change in the mindset of enterprises and other entities.
<h2>How to Assess Your Organization's AI Readiness?</h2>
AI readiness can be defined as an entity's ability to successfully implement AI technologies in its day-to-day operations. We look at the different aspects of verifying AI readiness below. 
<ul>
<li>Do Your Employees have the Skills to Utilize AI</li>
</ul>
Employees in your organization should have the knowledge and skills to utilize AI. Find out which individual lacks skills. Conduct training for those employees. If your organization is still not equipped with the necessary skills, consider hiring AI professionals having the necessary qualifications and relevant experience. As AI technology changes fast, you need to have refresher training sessions for keeping your staff up-to-date with the latest AI tools and technologies. Partner with quality education institutions to stay abreast of the latest AI advancements as well as developments.
<ul>
<li>Will Your Organization Obtain Acceptable ROI</li>
</ul>
Your organization should have the funds to implement AI. Before investing, determine what benefits your organization will get. Will the investment yield ROI? This is a very important factor. We now examine how to gain maximum ROI on your organization’s AI investment. First, list out AI activities based on their potential ROI. Next, eliminate the unfeasible ones. You can also come back to them later if they would be feasible in the future. Find out the effort for each approved activity. Arrange the activities that give the maximum value with the least work. First do the activities that are easiest to do and give relatively higher returns. This is both a smart and effective strategy.
<ul>
<li>Determine Technical and Data Feasibility</li>
</ul>
Find out if the relevant technology as well as tools to implement AI applications are available. For this your organization will have to expend both time and effort to do relevant research. It is a good idea to consult AI experts who can advise you better about the feasibility aspect. Your organization’s hardware, software as well as networking infrastructure should be able to handle the load of AI processes. AI applications require enormous computational power as well as storage. Your organization may have to upgrade its hardware and software to handle AI loads. This would entail significant investment in infrastructure and training to use the infrastructure efficiently and effectively.  
The organization's data needs to be enough, applicable and of acceptable quality to train AI models to perform assigned tasks. Determine whether your organization's data sources are available as well as accessible. The data should be complete in all aspects as well as clean. Otherwise, you have to cleanse the data utilizing quality data cleaning tools. Does your organization have good quality cybersecurity systems to identify and prevent data breaches? Is there mature data governance in place? An AI model’s output depends on the quality of data supplied to the former. In case of poor-quality data, the AI model will generate biased or unreliable or inaccurate data.  Are there measures for ensuring data privacy as well as data integrity?
Alson Read: <a href="https://coffeebeans.io/blogs/ai-in-healthcare-key-applications-and-future-potential" title="Website Blog">https://coffeebeans.io/blogs/ai-in-healthcare-key-applications-and-future-potential</a>
<ul>
<li>Ensure Data Meets Security and Compliance Aspects</li>
</ul>
It is necessary that the available data in your organization adheres to regulations such as GDPR (General Data Protection Regulation), CCPA (California Consumer Privacy Act), HIPAA (Health Insurance Portability and Accountability Act) and others that are applicable as per the nature of the data and where your organization is located. This way your organization will avoid paying substantial fines and gain the trust of its clients. This aspect is very important if your organization deals with critical sectors such as finance or healthcare. 
<ul>
<li>Change Management</li>
</ul>
Your enterprise should be ready to accept change in the form of AI implementation across the organization. The latter needs to craft an effective change management plan to ensure seamless AI implementation. All members of the enterprise should be educated or informed clearly regarding the advantages of AI implementation across the enterprise. Organize training programs catering to the learning style as well as requirements of each member.  The top management should be active as well as very positive about embracing the utilization of AI technology. Finally solicit feedback from members about any doubts or issues they have. This will enable the enterprise to incorporate the feedback and retain the trust of employees.
<ul>
<li>Cultural Readiness</li>
</ul>
Your organization’s employees may be reluctant to embrace AI because they may have to learn something new or they have the fear that adopting AI may cause them to lose their jobs. To tackle this obstacle consider having awareness programs designed to reassure employees.
<ul>
<li>Ease of Integration</li>
</ul>
Examine the existing infrastructure of your organization. If the latter has legacy systems it may be challenging to integrate the latest AI frameworks with it. Your organization may have to upgrade the existing infrastructure. Sometimes, tools that help ease integration may need to be purchased. 
<ul>
<li>Pilot Projects</li>
</ul>
It is a good idea to start off with small pilot projects. This way the top management can test the technology in real-world situations without taking big risks. If there are any issues, they need to be resolved prior to full-scale AI implementation. 
<ul>
<li>Crafting of a Well-Designed Roadmap</li>
</ul>
The latter needs to have achievable milestones and deadlines. Outline KPI (Key Performance Indicators) to measure success. There should be a carefully formulated plan for AI adoption across various departments in the organization.
<ul>
<li>Measure the Value Addition of AI Adoption</li>
</ul>
AI adoption should meet the goals and objectives of your enterprise. Your organization needs to define clear and measurable goals. At the onset examine KPIs. This could be savings of money and time, ramped up operational efficiency, superior sales and marketing as well as other aspects. Periodically examine the metrics to know exactly how much progress has happened. In the event of unsatisfactory performance, your organization needs to identify the cause and take corrective action.
<h2>The Process to Ensure that Your Organization's Data is Ready for AI</h2>
Data is the base of AI systems. Hence, it is imperative to have an effective data strategy in place. When AI models are trained on quality data the result is more reliable predictions. The latter enables organizations to make better decisions. We take a look at what constitutes good quality data. The latter deals with outliers effectively. The data is properly structured and organized. The former have proper labels and are well categorized. Data is complete in all aspects. Finally, the former is accurate as well as consistent.
Note that AI adoption is not a sole event. Rather it is a continuous activity which needs to be tweaked as your enterprise grows or changes or novel technologies need to be incorporated.
<h2>How to Go About Organizing AI Implementation?</h2>
If your organization possesses the in-house expertise to conduct AI readiness assessment, then do so at the earliest. Otherwise, it is recommended that your organization outsource the work to a company specializing in conducting thorough AI readiness assessment. AI implementation will give your organization a competitive edge in the market. For your information, adoption of AI is no longer an option rather it is mandatory to stay relevant. Otherwise, your organization will risk falling behind competitors who are leveraging the latest AI technology. Do due diligence before finalizing on a firm to conduct AI readiness assessment for your organization. Request for references and talk to past clients to evaluate the company’s performance as well as reliability. Do not make the fundamental mistake of hiring the first firm your organization comes across. Consult other stakeholders of your organization and evaluate multiple companies before making a final decision.
With relevant expertise and experience in conducting assessment of AI readiness for multiple organizations, CoffeeBeans is well positioned to meet your organization’s goals and objectives. We have a pool of talented professionals proficient in conducting AI readiness assessment for different organizations. They will evaluate AI readiness for your organization swiftly, systematically, and accurately. Our clients can vouch for our transparency, real-time response as well as adherence to stipulated deadlines. We provide stellar quality at competitive rates. Reach out to us at <a href="mailto:enquiries@coffeebeans.io">enquiries@coffeebeans.io</a> to know how we can help your organization obtain its specific and unique requirements and preferences.

Organizations are increasingly adopting AI (Artificial Intelligence) for the technology’s significant benefits and also to stay relevant in the intensely competitive market. But before implementation of AI across your organization you have to evaluate whether your organization has the necessary attributes for successful AI implementation.

How to Assess Your Organization's AI Readiness: A Comprehensive Guide

<a href="https://coffeebeans.io/consulting/ai?utm_source=wb&#x26;utm_medium=wb&#x26;utm_campaign=wb">AI (Artificial Intelligence)</a> has transformed many sectors including healthcare. As a rule, this sector always prefers to leverage the latest technology and was one of the early adopters of AI technology. The global AI medical market was valued at $20.9 billion this year. It is predicted to touch $148.4 billion by 2029 growing at a CAGR( Compounded Annual Growth Rate) of a significant 48.1%.
We look at some of the important applications of AI in the healthcare domain.
<img src="https://media.coffeebeans.io/Untitled-22.jpg" alt="">
<ul>
<li>Robotic Surgery</li>
</ul>
Hospitals utilize AI and robots to aid different types of surgeries. The surgeries aided by robots result in less surgery related complications, less time for patients to recover as well as patients experiencing less pain. In case of need, AI helps surgeons to operate on patients within confined spaces. AI powered robots can maneuver over fragile organs as well as tissues. This results in reduced blood loss, brings down the risk of infections as well as lower postoperative discomfort. Another important benefit is minimal scarring, a concern shared by many patients.
<ul>
<li>Managing Healthcare Data</li>
</ul>
AI has the capacity to process vast amounts of data. The former processes medical  information in healthcare organizations in minutes as compared to earlier methods that took a number of years. Hence healthcare administration takes less time and money. The efficiency of daily operations is boosted. Healthcare processes and tasks take less time and with less errors. Medical data is no longer restricted to silos and well connected and organized thanks to cutting-edge AI technology. 
<ul>
<li>Superior Patient Experience</li>
</ul>
AI provides timely appointment reminders and customized health advice to patients. Usage of AI results in quicker as well as higher degree of personalization of treatment of patients. Hospitals as well as doctors are able to treat relatively more number of patients thanks to AI. The medical history of the patient, current medical literature, the lifestyle of the patient, genetic profile of the concerned patient and the demographic data of the patient will be processed and analyzed by AI applications to give a highly personalized treatment plan for each patient.
<ul>
<li>Drug Discovery</li>
</ul>
Drug development usually takes many years in terms of time as well as costs billions of dollars in terms of money. At the end only a small percentage of drug candidates receive approval. Compared to humans, AI can better predict which drugs will succeed and which will have tolerable side effects. AI helps identify suitable candidates to participate in new drug trials. The technology can analyze vast datasets to pinpoint novel drug targets as well as predict the effectiveness of drugs and associated side effects. This aid scientists to craft highly effective clinical trials as well as introduce new drugs to market faster than before.
<ul>
<li>Superior Medical Diagnosis</li>
</ul>
AI technology is better than medical professionals in predicting as well as diagnosing disease in patients. The former is faster as well as more accurate in detecting illnesses. As such medical intervention can be done earlier resulting in superior outcomes for affected patients. By employing <a href="https://coffeebeans.io/blogs/machine-learning-in-banking--use-cases-and-implementation-process?utm_source=wb&#x26;utm_medium=wb&#x26;utm_campaign=wb">machine learning </a>algorithms, patterns can be identified in patient data that may be missed by human operators. This results in early detection of illnesses as well as superior health outcomes.
<ul>
<li>Training Medical Professionals</li>
</ul>
Virtual simulators created using AI provide training platforms where medical students as well as practitioners can practice surgical tasks and clinical activities. So, individuals aspiring to hone their medical skills get a safe platform as well as get feedback to improve their knowledge and expertise. AI helps healthcare personnel keep in touch with the latest medical developments as well as boost their skills and confidence.
<ul>
<li>Enhances Public Health</li>
</ul>
AI is able to detect disease outbreaks earlier compared to humans. ML (Machine Learning) models process epidemiological information, social media as well as medical records to detect the beginning of health disasters. Predictive analytics can suggest preventive steps including vaccination drives and dissemination of relevant information to warn the public. So, epidemics cause less damage and are better controlled thanks to AI technology. This contributes significantly to maintaining and enhancing the state of public health.
<ul>
<li>Enables Remote Patient Care</li>
</ul>
There may be locations where patients cannot access physical healthcare facilities. AI helps provide teleconsultations with qualified medical staff to patients residing in remote areas or areas that lack adequate medical facilities. Note that AI makes remote patient monitoring possible. It becomes possible to monitor the vital signs of patients, whether they are taking the prescribed medication as well as the current health condition of the patient. This is particularly beneficial for patients suffering from chronic disorders including diabetes, asthma, and others.
<ul>
<li>Superior Compliance with Existing Regulations</li>
</ul>
The privacy of patient data needs to be protected. Healthcare organizations must comply with existing regulations governing healthcare data such as HIPAA (Health Insurance Portability and Accountability Act) and others.  If there are data breaches patients lose trust in healthcare organizations. So, the latter experiences patient desertion as well as financial losses and reputational damage. AI is more capable and efficient than human operators in ensuring healthcare organizations adhere to complex and ever evolving relevant regulations. This is done faster, better and with greater accuracy as well as lesser errors compared to human individuals or teams. 
<ul>
<li>Superior Management of Healthcare Records</li>
</ul>
EHR (Electronic Health Records) using AI technology have made the record keeping process simple as well as convenient. Medical professionals and administrative staff get access to patient related data faster and more securely than before. By utilizing AI powered EHRs there is less load on administrative staff, less errors and superior quality of patient care. Thanks to AI EHRs, medical staff are able to make informed decisions related to patient treatment. Also, historical patient information can be utilized by ML (Machine Learning) algorithms to diagnose illnesses.  By leveraging AI, the process of recording patient data is easier and more efficient. This ramps up the productivity of healthcare related organizations as well as medical professionals.
<ul>
<li>Superior Prediction of Disease Outbreaks</li>
</ul>
For your information, predictive analytics is a field of AI that makes predictions based on historical data. Predictive Analytics is very useful in accurately predicting major and minor disease outbreaks well before they actually become big or unmanageable. This allows for targeted intervention limiting the spread as well as impact of the disease outbreak. Big data is used along with AI for superior performance in detecting potential epidemics. Public health department can also allocate resources to tackle potential epidemics in an efficient and prioritized manner. As a result, a considerable amount of time, money and effort are saved and public health safety is ensured.
<ul>
<li>Improved Processing of Insurance Claims</li>
</ul>
It is a well-known fact that there are many incidents of fraudulent claims. These include unnecessary tests and treatments, inflated bills, and others. AI analyses vast amounts of data in very less time to detect frauds. As such genuine applicants experience faster claim processing. Insurers are also happy by lower fraudulent claim payments. By leveraging AI, the medical insurance claim process becomes smooth and offers peace of mind to the insured parties. Insurers save money and genuine claims are fully reimbursed.
<ul>
<li>Optimization of Healthcare Supply Chains</li>
</ul>
If the supply chain of a healthcare entity is not proper there will be incidents of shortages of medical supplies that can endanger the health or life of patients. On the other hand overstock scenarios can result in wastage as well as monetary loss for the healthcare organization. By leveraging AI, healthcare entities can effectively avoid such situations. AI is able to predict demand for supplies accurately and quickly.
<ul>
<li>Superior Analysis of Medical Images</li>
</ul>
AI is better and faster than humans to identify anomalies in X-rays, MRI (Magnetic Resource Imaging)s and CT (Computed Tomography) scans. The former utilizes deep learning algorithms to pinpoint subtle abnormalities missed by human operators. This results in earlier detection of medical issues as well earlier treatment leading to improved outcomes for respective patients.
<ul>
<li>Free Medical Staff from Administrative Tasks</li>
</ul>
AI applications do the full billing process, scheduling of appointments as well as answer basic and advanced patient queries. RPA (Robotic Process Automation) is increasingly being utilized to handle insurance claims of patients as well as management of medical records. AI is faster and more efficient in handling and completing administration related tasks. As such doctors and nurses can focus on healthcare and devote the saved time to more critical functions. 
<h1>Future Potential</h1>
AI technology is improving by leaps and bounds as companies are investing heavily in improving the former. More and more healthcare organizations will begin to realize the benefits of adopting AI technology. The future will see better AI technology and increased adoption of the same by healthcare entities. The result is better outcomes for patients, reduced administrative burden, increased use of robots in surgery and improved accessibility to high quality health services. Both healthcare providers and patients will benefit on a significant scale.
If your healthcare organization is not utilizing AI, now is the time to adopt AI technology into your organization's systems, processes as well as operations. Healthcare entities just cannot afford to ignore AI. By adopting AI, healthcare institutions will experience ramped up productivity, revenue, profits as well as enhanced brand image and reputation. Your organization will be harnessing state-of-the-art technology and obtain a competitive edge in the market.
Possessing extensive expertise and experience in the healthcare domain as well as AI technology, CoffeeBeans is strategically positioned to integrate AI technology with healthcare organizations. We have a pool of talented professionals who have domain knowledge of healthcare as well as have mastery over AI technology. CoffeeBeans offers stellar quality at competitive rates. Our clients will vouch for our transparency, real-time response as well as adherence to stipulated deadlines. Reach out to us at <a href="mailto:atenquiries@coffeebeans.io">enquiries@coffeebeans.io</a>  to know how we can help your organization achieve your specific and unique goals and objectives.

AI (Artificial Intelligence) has transformed many sectors including healthcare. As a rule, this sector always prefers to leverage the latest technology and was one of the early adopters of AI technology. The global AI medical market was valued at $20.9 billion this year. It is predicted to touch $148.4 billion by 2029 growing at a CAGR( Compounded Annual Growth Rate) of a significant 48.1%.

AI in Healthcare: Key Applications and Future Potential

DAM (Digital Asset Management) involves the organization, storage and distribution of digital assets including photos, videos, graphics as well as documents. For your information the Digital Assets market is expected to show a revenue growth of 17.38% in the coming year. As per Grand View Research the global digital asset management market size was valued at USD 4.22 billion in 2023 and is projected to grow at a CAGR of 16.2% from 2024 to 2030. Digital asset management systems refer to software solutions that aid organizations to manage respective digital content in a centralized, secure and scalable manner. The technology enables organizations to enhance content, become more efficient and productive as well as ramp up the customer experience. DAM makes it possible for users to search, retrieve and share relevant digital assets. The former's advantages include real-time collaboration. Solutions utilizing DAM have powerful functionality to add metadata, optimize workflows as well assign permissions. The technology is utilized to make sure that the right assets are shared with authorized individuals at the right time.
Digital assets of organizations include product photos, pitch decks and marketing material. Thanks to digit asset management tools enterprises can employ client data as well as insights fully for superior decision making. For your knowledge, the usage of digital asset management is on the rise. The technology is constantly improving as well as evolving. The former leverages cutting edge technologies such as AI (Artificial Intelligence) to deliver better value as well as results. More and more organizations are leveraging Digital asset management <a href="https://coffeebeans.io/blogs/transforming-software-development-and-operations-with-ai-powered-devops?utm_source=WEBSITE%20BLOG&#x26;utm_medium=WEBSITE%20BLOG&#x26;utm_campaign=WEBSITE%20BLOG">software</a> in their systems as well as day-to-day operations.  One important application of DAM software is efficient as well as effective administration. The fintech industry has to adhere to constantly changing regulations. Earlier this process was manual but thanks to DAM, it has become automated making the process far faster and relatively error free. The fintech sector is usually among the earliest to adopt state-of-the-art technologies. Hence it is increasingly integrating DAM into existing systems, processes as well as operations. This trend is likely to only increase in the future. We examine the benefits and relevant use cases in fintech associated with this exciting and powerful technology. The latest trend is cloud-based DAM solutions that enable seamless scalability.
<img src="https://media.coffeebeans.io/website%201.jpg" alt="">
<h1>Six Transformative Benefits of Digital Asset Management in FinTech</h1>
<ol>
<li>Ease of Integration with Third-party Apps</li>
</ol>
Digital asset management solutions ramp up productivity by smoothly integrating with external useful tools as well as applications. By merging Digital Asset Management applications with other parts of a financial organization including content management systems, customer relationship management modules and others the financial institutions become more agile and efficient.
2. Superior Multichannel Distribution
Contemporary clients interact with financial companies through different channels as well as formats such as websites, social media as well as emails. Digital Asset Management tools make easy the procedure of adapting as well as distributing assets across various channels. Thanks to digital asset management tools financial companies can conveniently repurpose assets for particular platforms. This enables a consistent brand image as well as user experience in all interactions. By superior management of the distribution of relevant assets, financial firms can provide quality content ramping up customer retention in various digital channels.
3. Personalization and Focused Marketing
Digital asset management tools help finance related companies to utilize client data as well as insights skillfully for superior decision making. Through utilizing the analytics abilities of digital asset management tools financial firms can obtain useful data pertaining to client preferences, unique behavior as well as other patterns. Using this data, personalized and focused marketing campaigns can be launched. Through supplying customized content as well as experiences to clients, financial companies can ramp up client satisfaction as well as retention. The technology helps financial firms adapt and fine-tune respective marketing plans utilizing real-time insights.
4. Superior Analytics
Numerous digital asset management tools possess robust analytics features that supply useful insights related to asset performance as well as usage. Through monitoring metrics including downloads, unique views as well as shares financial companies get superior comprehension of asset performance, client preferences as well as feedback on content quality. The data-driven strategy results in informed choices as well as fine tuning of digital assets with the aim of superior results.
5. Superior Management of Rights
 The technology helps superior management of digital rights. This is achieved by monitoring expiration dates, usage permissions as well as licenses of different assets. Financial entities are able to achieve legal compliance, avoid copyright infringements as well as be free of potential legal ramifications. Thanks to the technology, users can conveniently pinpoint as well as manage assets with limited usage rights and protect their company from legal action.
6. Save Significant Time and Money
Repetitive work including recreating assets which consumes a lot of resources in terms of time and effort. The technology has sophisticated automation functionality including file conversions which prepares assets for diverse platforms as well as channels. This minimizes the workload on the creative workforce. Also, the accessibility as well as reusability of creative assets minimizes expenditure related to licensing by eliminating unwanted duplicate purchases.
<h1>Six Use Cases of Digital Asset Management in FinTech</h1>
<ul>
<li>Makes Remote Work Possible and Better</li>
</ul>
With remote working becoming more widespread, the requirement for DAM software has risen. The latter makes individuals operating from different locations collaborate easily as well as conveniently. They get permissions to access relevant assets from any geographical location and any time of the day. By leveraging cloud capabilities, DAM systems can accommodate more employees seamlessly.
<ul>
<li>Makes the Search and Retrieval Process Easier</li>
</ul>
DAM adds meta tags to digital assets as well as stores the assets in a centralized repository. Hence users are able to search and retrieve needed assets quickly and conveniently. Hence, data is not siloed. For your information metadata refers to data about data. 
<ul>
<li>Protects the Privacy and Confidentiality of Data</li>
</ul>
DAM technology assigns role-based permissions. The former determines who can access which asset, what they can do with the asset such as viewing, altering, sharing, or downloading. The permissions assigned depend on the position and responsibility of the person in the financial organization.
<ul>
<li>Superior Tracking of Changes Made to Important Documents</li>
</ul>
Many persons view and make changes to important documents in financial organizations. It is easy to lose track of who made the concerned changes and when. Digital asset management systems have the features as well as functionality to implement superior version control. So, authorized individuals can see which person in the organization made the changes and when they did it. This is an important and valued application of DAM technology to ensure transparency and traceability. Thanks to DAM technology, the staff of financial firms always get the latest as well as approved versions of important files.
<ul>
<li>Superior Regulatory Compliance</li>
</ul>
The Fintech companies have to handle vital digital assets including financial documents as well as customer data. There exists national as well as international regulations that fintech companies have to adhere to. Failure to do so results in a high amount of fines as well as reputational downgrade among clients. Examples of important regulations are HIPAA, PCI-DSS as well as GDPR. DAM technology helps financial companies avoid the legal repercussions of failing to adhere to the relevant regulations. 
<ul>
<li>Ease of Scalability</li>
</ul>
With the passage of time even small fintech organizations grow and expand. Quality DAM applications have the capability to handle increased loads of digital assets while maintaining the high performance expected by stakeholders. So, efficiency is ensured even if a particular financial company becomes bigger. DAM systems leverage cloud technology to meet any rise in requirements or client expectations. 
Other benefits of utilizing DAM systems and technology include ensuring that organizations do not have to duplicate assets, retiring outdated assets and full compliance with licensing agreements. DAM results in minimizing the time, effort, and difficulty in conducting audits which give financial organizations peace of mind. It is important to train the staff on how to use DAM and extract maximum benefit from this powerful technology.
If your organization belongs to the Fintech industry now is the right time to adopt digital asset management technology. If you have the in-house capability, use it to craft useful digital asset management solutions that will help your organization meet its existing targets as well as short-term and long-term goals.  In the event your organization lacks the expertise, outsource the work of building high quality digital asset management software to external players who are proficient in the same. Adoption of DAM will give your organization a significant edge in the fiercely competitive market.
Possessing the relevant expertise as well as experience in digital asset management, CoffeeBeans is well positioned to help your organization meet its specific and unique goals as well as objectives. We have a pool of professionals with proficiency in cutting-edge software technology as well as domain expertise in Fintech. They are familiar with the nuances of implementing DAM technology and have experience to deal with the associated challenges. Our clients will vouch for our transparency, real-time response as well as adherence to stipulated deadlines. We provide stellar quality at competitive rates. Reach out to us at <a href="mailto:enquiries@coffeebeans.io">enquiries@coffeebeans.io	</a>to know how we can help you achieve your short-term as well as long-term goals and targets.

DAM (Digital Asset Management) involves the organization, storage and distribution of digital assets including photos, videos, graphics as well as documents. For your information the Digital Assets market is expected to show a revenue growth of 17.38% in the coming year.

Top 6 Transformative Benefits and Use Cases of Digital Asset Management in FinTech

Introduction
In recent years, Large Language Models (LLMs) like GPT, BERT, and their derivatives have transformed the AI landscape by enabling powerful applications in natural language processing (NLP). However, these models often face challenges in generating accurate, relevant, and domain-specific responses, particularly when dealing with niche knowledge areas. Retrieval-Augmented Generation (RAG) emerges as a robust solution to this limitation by combining the generative capabilities of LLMs with a retrieval system that fetches relevant, context-specific information.
This blog delves into <a href="https://bit.ly/3VqbiQI" title="CoffeeBeans">fine-tuning RAG</a> for domain-specific applications, exploring the architecture, implementation, best practices, and potential future developments.
<img src="https://media.coffeebeans.io/Untitled-3.jpg" alt="">
<h2>Understanding Retrieval-Augmented Generation (RAG)</h2>
<h3>What is RAG?</h3>
RAG is a hybrid model architecture that combines:
<ul>
<li>Retriever: A system that searches and retrieves relevant documents or knowledge snippets from a database, knowledge graph, or API.</li>
<li>Generator: A language model that uses the retrieved information to generate responses, ensuring relevance and factual accuracy.</li>
</ul>
This synergy allows RAG models to leverage external knowledge bases, improving response precision and reducing the risk of hallucinations.
<h2>Why Fine-Tune RAG for Domain-Specific Applications?</h2>
Fine-tuning a RAG model for domain-specific use cases enhances its ability to:
<ul>
<li>Address Highly Specialized Queries</li>
</ul>
RAG fine-tuning enables precise responses to niche or technical questions by incorporating domain-specific knowledge directly into the retrieval and generation pipeline. This ensures the model caters to industry-specific jargon and unique problem statements.
<ul>
<li>Generate More Context-Aware, Accurate, and Actionable Insights</li>
</ul>
By leveraging relevant external knowledge, RAG models enhance the contextual understanding of queries, improving the quality and applicability of the generated outputs. This makes the insights actionable and reliable for decision-making in specialized fields.
<ul>
<li>Integrate Proprietary or Confidential Knowledge Bases</li>
</ul>
Fine-tuned RAG models can incorporate secure and private datasets, ensuring compliance with data governance while generating responses. This allows organizations to utilize sensitive information without exposing it to external systems.
<ul>
<li>Reduce Dependency on Generic, Pre-Trained Knowledge</li>
</ul>
Generic LLMs may lack depth in specific areas, but fine-tuning RAG reduces this reliance by enriching responses with targeted data sources. This customization ensures the model is aligned with organizational goals and domain expertise.
Examples of domain-specific applications include:
<ul>
<li>Healthcare: Assisting doctors with diagnosis by referring to medical literature.</li>
<li>Legal: Providing case-law references for legal professionals.</li>
<li>Finance: Answering questions about market trends using proprietary reports.</li>
<li>Education: Offering tailored academic content and explanations.</li>
</ul>
<h2>Core Architecture of RAG</h2>
<img src="https://media.coffeebeans.io/rag-arch.png" alt="">
<h3>1. Retriever</h3>
The retriever locates relevant documents based on the input query. Common approaches include:
<ol>
<li>Sparse Retrieval: Techniques like TF-IDF or BM25 that rely on keyword matching.</li>
<li>Dense Retrieval: Leveraging neural networks, such as Sentence-BERT, to encode queries and documents into dense vector spaces for similarity searches.</li>
</ol>
<h3>2. Generator</h3>
The generator, typically an LLM, uses the query and retrieved context to produce the final output. Examples include:
<ul>
<li>OpenAI’s GPT Family</li>
</ul>
The GPT series, including models like GPT-4, are highly versatile and widely used for text generation. These models excel in natural language understanding and generation tasks, making them a popular choice for building RAG systems due to their pre-trained capabilities on vast datasets.
<ul>
<li>Google’s T5 (Text-to-Text Transfer Transformer)</li>
</ul>
T5 converts all NLP tasks into a text-to-text format, enabling consistent processing for various applications like translation, summarization, and classification. Its flexibility and efficiency make it a strong candidate for RAG systems requiring domain-specific text processing.
<ul>
<li>Meta’s LLaMA (Large Language Model Meta AI)</li>
</ul>
LLaMA focuses on delivering high performance with smaller, more efficient models, making it suitable for specialized deployments. It offers competitive results in text generation and understanding tasks, making it a cost-effective option for domain-specific RAG implementations.
<h3>3. Knowledge Base</h3>
The retriever queries a structured or unstructured knowledge base, which could be:
<ul>
<li>Document Store (e.g., Elasticsearch)</li>
</ul>
A document store organizes data in a searchable format, often optimized for text-based queries. Elasticsearch, a widely used solution, allows efficient full-text searches across structured and unstructured documents, making it ideal for RAG systems that rely on keyword-based retrieval.
<ul>
<li>Vector Database (e.g., Pinecone, FAISS)</li>
</ul>
Vector databases store data as dense embeddings, enabling similarity searches based on contextual relevance rather than exact matches. Pinecone and FAISS are popular choices, offering fast and scalable vector retrieval, crucial for dense retrieval in RAG models.
<ul>
<li>API or External Knowledge Graph</li>
</ul>
APIs and knowledge graphs provide structured, dynamic data that can enrich retrieval with interconnected concepts. External knowledge sources, like Wikidata or proprietary APIs, allow RAG systems to fetch real-time, domain-specific, or highly relational information for accurate responses.
4. Fusion of Multiple Retrieved Sources in RAG
RAG’s fusion mechanism aggregates information from various retrieved documents or data points, ensuring that all relevant contexts are considered. This process synthesizes insights, resolves conflicts, and eliminates redundancies to provide a cohesive, contextually rich, and accurate response tailored to the input query.
<h2>Steps to Fine-Tune a RAG Model</h2>
Fine-tuning RAG involves customizing both the retriever and generator to align with domain-specific needs. Below are the key steps:
<img src="https://media.coffeebeans.io/fine%20tuneing.png" alt="">
1. Preparing the Dataset
Collect Domain-Specific Documents, FAQs, Manuals, or Other ResourcesGathering relevant and high-quality domain-specific data is crucial for training a RAG model. These sources can include technical manuals, industry reports, frequently asked questions (FAQs), academic papers, and proprietary documents, which serve as the knowledge base from which the retriever fetches information.
<h4>Preprocess the Data</h4>
<h4>Effective data preprocessing ensures that the collected documents are in a format suitable for both retrieval and generation tasks:</h4>
<ul>
<li>Tokenize and Clean Text: Tokenization breaks down the text into smaller units (words or subwords), while cleaning removes noise like special characters, irrelevant symbols, or extra spaces.</li>
<li>Extract Relevant Entities or Concepts: Named entity recognition (NER) and concept extraction techniques help identify important domain-specific terms (e.g., medical conditions, legal terms) that enhance the retriever’s ability to fetch precise, context-relevant information.</li>
</ul>
<h3>Organize into Retriever-Friendly Formats</h3>
To ensure efficient retrieval, the processed data must be transformed into formats that optimize the retrieval process:
<ul>
<li>Embeddings: Convert documents into vector representations using models like BERT or Sentence-BERT, making them searchable in dense vector databases.</li>
<li>Indexed Structures: For sparse retrieval, organize the data into inverted indexes, where keywords and their document associations allow for efficient search and ranking based on relevance.</li>
</ul>
<h3>2. Fine-Tuning the Retriever</h3>
Fine-tuning the retriever ensures that the retrieval system is optimized for domain-specific queries, improving relevance and accuracy. This can be done using sparse and dense retrieval techniques.
Sparse Retriever Fine-Tuning
<ul>
<li>Optimize Ranking Algorithms (e.g., BM25 parameters): Fine-tune parameters like k1 and b in BM25 to adjust how document relevance is ranked based on term frequency and document length, improving the retrieval of relevant documents in a domain-specific context.</li>
<li>Implement Domain-Specific Tokenizers or Stop-Word Lists: Customize tokenizers to handle domain-specific terms and abbreviations, while adjusting stop-word lists to filter out irrelevant words, improving the retriever’s accuracy in specialized areas.</li>
</ul>
<h4>Dense Retriever Fine-Tuning</h4>
<ul>
<li>Train Sentence Transformers Using Contrastive Learning: Train models like Sentence-BERTwith contrastive learning to distinguish between relevant and irrelevant document pairs, ensuring better semantic relevance for domain-specific queries.</li>
<li>Fine-Tune Pre-Trained Models (e.g., MiniLM, Sentence-BERT): Fine-tune pre-trained models on domain-specific query-document pairs, allowing them to better capture the nuances and vocabulary of the field, improving retrieval precision.</li>
</ul>
Fine-tuning both approaches enhances the retriever's ability to provide accurate and context-aware results in domain-specific applications.
<h3>3. Fine-Tuning the Generator</h3>
<ul>
<li>Supervised Fine-Tuning with Domain-Specific QA Pairs</li>
</ul>
Fine-tuning with domain-specific question-answer pairs enables the model to learn the context and specificities of a particular field, improving its ability to generate accurate, relevant answers tailored to that domain.
<ul>
<li>Reinforcement Learning for Prioritizing Factual Correctness</li>
</ul>
Reinforcement learning (RL) techniques can enhance the model by rewarding accurate, domain-relevant responses, guiding the model to prioritize factual correctness and relevance in the answers it generates.
<ul>
<li>Efficient Fine-Tuning with LoRA and PEFT</li>
</ul>
Techniques like LoRA (Low-Rank Adaptation) and PEFT (Parameter-Efficient Fine-Tuning) allow large models to be fine-tuned with limited computational resources by focusing on a small set of parameters, making domain adaptation more resource-efficient.
<h3>4. Integrating the Knowledge Base</h3>
<ul>
<li>Index the domain-specific dataset into a retrievable format:</li>
<li>Use vector stores like Pinecone for dense embeddings.</li>
<li>Implement traditional inverted indices for sparse retrieval.</li>
<li>Ensure the knowledge base is regularly updated.</li>
</ul>
<h3>5. Evaluating Performance</h3>
Key evaluation metrics include:
<ul>
<li>Precision@K: How relevant the top K retrieved documents are.</li>
<li>BLEU/ROUGE Scores: For assessing the quality of generated text.</li>
<li>Domain-Specific Metrics: Accuracy in specific contexts (e.g., clinical accuracy for healthcare models).</li>
</ul>
<h2>Challenges and Solutions</h2>
<h3>1. Limited Domain Data</h3>
<ul>
<li>Solution: Use data augmentation techniques such as paraphrasing or synthesizing queries.</li>
</ul>
2. Model Complexity
<ul>
<li>Solution: Use lightweight fine-tuning approaches like LoRA to minimize resource demands.</li>
</ul>
<h3>3. Keeping the Knowledge Base Updated</h3>
<ul>
<li>Solution: Automate the ingestion and indexing process for new documents.</li>
</ul>
<h3>4. Handling Ambiguity or Low-Quality Data</h3>
<ul>
<li>Solution: Implement confidence thresholds and fallback mechanisms to gracefully handle ambiguous queries.</li>
</ul>
<h2>Best Practices for Fine-Tuning RAG</h2>
<ul>
<li>Start Small: Begin with a limited dataset and scale gradually to avoid overfitting.</li>
<li>Leverage Pre-Trained Models: Use pre-trained retrievers and generators as a foundation.</li>
<li>Iterative Optimization: Fine-tune in cycles, alternating between retriever and generator for incremental improvement.</li>
<li>User Feedback Loops: Incorporate end-user feedback to refine model responses.</li>
</ul>
<h2>Applications of Fine-Tuned RAG</h2>
<ul>
<li>Healthcare</li>
</ul>
Assist in diagnosis and treatment planning using up-to-date medical literature.
<ul>
<li>Legal</li>
</ul>
Generate case summaries or legal advice based on jurisprudence.
<ul>
<li>Customer Support</li>
</ul>
Enhance chatbots with domain-specific FAQs and troubleshooting guides.
<ul>
<li>Education</li>
</ul>
Provide detailed explanations for academic queries, personalized to a student's curriculum.
<ul>
<li>Enterprise Knowledge Management</li>
</ul>
Answer employee questions by leveraging internal documentation.
<h5>Future Scope of RAG in Domain-Specific Applications</h5>
<h4>1. Contextual Awareness</h4>
Future RAG models will enhance their contextual understanding by integrating multi-modal inputs (e.g., text, images, audio). This will enable them to generate richer, more nuanced responses that consider diverse forms of data for a deeper comprehension of queries.
<h4>2. Real-Time Updates</h4>
With automated pipelines for real-time knowledge base indexing, RAG models will ensure they always access and provide the most up-to-date information, making them more reliable and relevant for dynamic, fast-changing fields.
<h4>3. Explainability</h4>
Improving the transparency of both the retrieval and generation processes will increase trust in RAG systems, particularly in high-stakes domains such as healthcare and law, where understanding how decisions are made is crucial for accountability and compliance.
<h4>4. Scalability</h4>
Future RAG models will be optimized for low-resource environments, including edge devices and on-premise deployments, making them more accessible and practical for a wider range of applications without relying on high-powered infrastructure.
<h4>5. Specialized Architectures</h4>
Domain-specific RAG models will emerge, tailored to industries such as healthcare (e.g., HIPAA-compliant models) or finance, enabling more precise and context-aware responses while adhering to industry regulations and requirements.
<h2>Conclusion</h2>
Fine-tuning RAG for domain-specific applications represents a transformative step in applying LLMs to real-world problems. By combining the retrieval power of dense or sparse methods with the generative capabilities of LLMs, RAG systems can bridge the gap between generic AI and highly specialized knowledge.
The road ahead is promising, with opportunities to refine models for better accuracy, explainability, and adaptability. Organizations looking to adopt RAG should invest in high-quality data curation, iterative fine-tuning processes, and robust evaluation frameworks to maximize the potential of this cutting-edge technology.

In recent years, Large Language Models (LLMs) like GPT, BERT, and their derivatives have transformed the AI landscape by enabling powerful applications in natural language processing (NLP). However, these models often face challenges in generating accurate, relevant, and domain-specific responses, particularly when dealing with niche knowledge areas. Retrieval-Augmented Generation (RAG) emerges as a robust solution to this limitation by combining the generative capabilities of LLMs with a retrieval system that fetches relevant, context-specific information.

Fine-Tuning Retrieval-Augmented Generation (RAG) for Domain-Specific Large Language Models

Once regarded as expensive and risky to adopt, more and more banks are adopting Machine Learning (ML) technology to enhance their day-to-day operations. The global machine learning in <a href="https://bit.ly/3Ooocec">banking</a> market size was valued at $1.33 billion in 2021 and is projected to reach $21.27 billion by 2031, growing at a CAGR of 32.2% from 2022 to 2031. Approximately 40% of financial services companies rely on ML for fraud detection. By implementing machine learning technology in their operations banks get a competitive edge as well as access to state-of-the-art technology. ML has transformed the way modern banks function and operate making them safer, better and faster. 
We look at the different use cases of machine learning in banking today.
<img src="https://media.coffeebeans.io/cover-2.jpg" alt="">
<ol>
<li>Risk Assessment</li>
</ol>
Machine learning technology reduces the time as well as risks for banks to approve loans. The former makes possible superior credit scoring. For your information, credit scoring tools analyze data from social media, telecommunication records, bank transactions and tax transactions to get a comprehensive overview of a client applying for a loan in a matter of minutes. Work experience, utility payments, rent history and annual income are other factors in determining the creditworthiness of applicants. Credit scoring tools are in great demand as there are many unbanked individuals worldwide and many are not considered creditworthy by traditional methods. ML algorithms can conduct highly accurate and customized credit score assessments. As a result, more individuals become eligible for credit. Humans may be biased and make unfair credit assessments. However, ML models make objective, fair and unbiased credit decisions. This bolsters the reputation of the bank among applicants. 
2. Investment Planning
Machine Learning algorithms process historical as well as real time financial data to identify patterns as well as trends better than earlier methods. Predictive analytics benefit from this and supply investors with superior insights into future market trends as well as stock values. Algorithmic trading has existed since the 80s. However, merging the same with ML has transformed it and made it more efficient as well as effective. Trading organizations leverage ML driven algorithmic trading that keeps track of finance related news, changing prices as well as trade results and other information. As a result, trades can be executed at the maximum prices utilizing that information.
3. Stock Market Forecasting
With the existence of vast historical data, machine learning tools can predict stock market trends. This includes identifying future risks as well as forecasting stock prices. Using machine learning the investors come to know of the slightest price fluctuations. Thanks to ML algorithms, informed decisions can be done in very less time.  
4. Customer Retention
Banks use machine learning to automate as well as minimize the time taken by customer support services. ML has the capability to analyze vast amounts of data to identify the needs and preferences of clients. Using these insights banks can come up with personalized recommendations for each client. ML tools learn from past experience as well as become better with time. As such the customer service offered by banks gets better and better. Better user experience leads to satisfied customers fostering customer retention. ML technology enables banks to obtain detailed insights about how their clients use their products and services. The data supplied by ML help banks track as well as predict client churn from changes in their behavior. The relevant clients are targeted with reminders and offers to encourage them to stay with the bank. The bank staff can reach out to them to understand the reasons for their dissatisfaction and take steps to address their grievances. 
5. Chatbots
Many banks make use of ML driven chatbots. They give real-time responses to client queries and issues. Note that their response becomes better with time. This leaves bank customer service staff free to focus on more strategic and innovative activities. Unlike human support staff, chatbots work round the clock. Chatbots leveraging advanced ML algorithms can resolve several daily queries which normally would be escalated to conventional customer support. If chatbots are unable to resolve a particular issue it would transfer it to a human support staff. However, before doing that the ML algorithm would classify the issue and extract relevant info so that the human staff can address the issue relatively quickly.
6. Adherence to Regulations
There exist many regulations that banks must adhere to. Also, the regulations keep evolving. Banks face a challenge to comply with the regulations. Human operators take a long time to understand the existing regulations and ensure that the banks adhere to them. The former are also prone to error. Machine learning solutions offer a better alternative. The former go through the regulations and documents swiftly enabling quick and full compliance. So, banks do not have to pay big fines for non-compliance.
7. Anti-Money Laundering
ML is very useful to detect and prevent money laundering activities. The ML algorithms can monitor vast numbers of transactions in real time and detect suspicious activity. The former flag such activities in real time for the bank to take appropriate action. Different patterns such as alterations in transaction frequency, abnormally large transactions, payments to high-risk jurisdictions are examples of suspicious financial activity. There exist layering processes, a frequent money laundering practice where multiple transactions are done to hide the origin of the funds. Smurfing is a popular money laundering tactic in which big amounts are broken down into smaller amounts that would attract a lesser amount of attention. ML can detect money laundering activities better than humans as well as traditional methods of identifying and preventing money laundering transactions.
8. Fraud Detection
By incorporating ML solutions, banks can detect fraudulent activities as well as identify users in real time. Credit card fraud can be detected as well as prevented in real time. ML tools alert banks in real time of cyberattacks making it possible for the banks to take mitigating measures and preventing any damage. The technology keeps banks a step ahead of criminals. ML makes the identity verification process simpler thus making clients happy.
9. Underwriting of Loans and Insurance
Humans consume tens of thousands of hours to do underwriting. There exists the possibility of human errors as well. Usually underwriting makes use of manual procedures as well as rule-based models which take a lot of time and are vulnerable to errors. ML technology can do underwriting much faster than humans slashing the time taken to approve or turn down loans. 
10. Managing False Positives
When banks wrongly reject requests for legitimate financial transactions it is called 'false positives'. The latter causes distress to clients. These lead to loss of revenue as well as client loyalty. Machine learning technology minimizes or eliminates false positives. These lead to happy and loyal clients as well as protect the banks' revenue and profits.
11. Onboarding
ML is extremely useful for onboarding. Clients get to open a bank account in a matter of minutes. ML tools verify their documents in real time. Thanks to the short and seamless onboarding process banks leave a good first impression on new clients. Regulatory compliance is done by ML by analyzing documents such as ID cards, income proof as well as address proof.
12. Processing of Documents
Classification of documents is an important task needing a lot of time as well as resources. ML greatly minimizes the time for the labeling, classification as well as organization of documents. By using OCR (Optical Character Recognition) technology, ML algorithms can digitize the text present on scanned documents. ML is utilized in document intensive procedures such as to extract relevant information from loan applications, identify fake documents as well as automate the process of claiming insurance.
After learning about machine learning use cases in the banking sector, we take a look at a step-by-step guide to implementing machine learning in banking.
<h1>Implementation of ML in banking</h1>
<ul>
<li>Have Clear Goals</li>
</ul>
Initially define your goals. What solution are you providing? Are you providing a better product or service? The goals will determine the ML algorithm you choose. It will also result in clarity of vision.
<ul>
<li>Obtain and Prepare Quality Data</li>
</ul>
The quality of the data matters. Source data from different avenues such as trends in the market, customer interactions and others. The data should be accurate as well as complete. Next clean the data. Manage missing data as well as lack of consistency.
<ul>
<li>Shortlist ML Algorithms to Use</li>
</ul>
There exist supervised learning algorithms as well as unsupervised learning algorithms. The ML algorithm you utilize is based on your goals and data type.
<ul>
<li>Do Training and Evaluation</li>
</ul>
After choosing the algorithm, feed it with data. Test for the performance of the model for parameters including accuracy as well as recall.
<ul>
<li>Deployment</li>
</ul>
After the testing stage, deploy the model. Merge the latter into your systems. Track the performance model for reliability and accuracy. In the event of unsatisfactory results do retraining utilizing fresh data.
<ul>
<li>Fix Ethical Issues</li>
</ul>
The model should be free of biases and discrimination. Check the model periodically and take action to eliminate biases. Make sure the model is fair as well.
<ul>
<li>Constantly Improve</li>
</ul>
As needs change or there is fresh data, implement the necessary improvement in the ML models. Keep track of fresh developments in the ML space.
With extensive expertise and experience in the banking domain as well as ML technology, <a href="https://bit.ly/3MytLWi">CoffeeBeans</a> is well placed to help you achieve your specific and unique goals. We provide stellar quality at competitive rates. Our clients can vouch for our transparency, real-time communication as well as adherence to stipulated deadlines. Reach out to us at <a href="mailto:enquiries@coffeebeans.io">enquiries@coffeebeans.io</a> to know we can help you achieve your objectives and preferences.

Once regarded as expensive and risky to adopt, more and more banks are adopting Machine Learning (ML) technology to enhance their day-to-day operations. The global machine learning in banking market size was valued at $1.33 billion in 2021 and is projected to reach $21.27 billion by 2031, growing at a CAGR of 32.2% from 2022 to 2031

Machine Learning in Banking- Use Cases and Implementation Process

Cyberattacks are becoming more frequent and sophisticated nowadays. There are phishing attacks that trick individuals into revealing confidential information.<a href="https://bit.ly/4eq4p8A"> AI(Artificial Intelligence)</a>  is used by hackers to mount relatively more sophisticated attacks. There are ransomware attacks where a system or whole network is locked. The attacker demands money to restore the system. The problem is that even if you pay the sum there is no guarantee the attacker will honor the commitment. There are DoS (Denial of Service) attacks where the system is overwhelmed with traffic. For your information, the global cybersecurity market will reach $207.77 billion by the end of this year. It is expected to reach $376.55 billion by 2029 with a CAGR (Compounded Annual Growth Rate) of 12.63% from 2024 to 2029. Half of the small and medium sized enterprises have become victims of cyber-attacks and more than 60% of those attacked eventually go out of business.
Note that cyberattacks are not mounted on only large businesses. Hackers target small and medium businesses too because they have limited cybersecurity. In the present era, basic cybersecurity will not do. As the attackers become more sophisticated there is a corresponding need for strong cyber security measures.
We look at 5 cybersecurity measures for businesses in 2025
<img src="https://media.coffeebeans.io/cover1.jpg" alt="">
<ol>
<li>Periodic Security Audits and Ethical Hacking</li>
</ol>
Constant improvement of security levels as well as vigilance should be a priority. Periodic security audits find out whether enterprises are following industry standards. The former also identifies and fixes existing vulnerabilities. Penetration testing involves real-word attacks and reveals vulnerabilities that could be used by hackers. This way enterprises can be a step ahead of cyber threats, stay up-to-date with regulations as well as defend their systems against cyber threats.
2. End-to-End Encryption
Today enterprises are online and likely to use the cloud. End-to-end encryption ensures that sensitive data while in transit is not compromised. The former is used to make sure that data in transit cannot be read by any entity other than the recipient. Even if hackers get access to the data while being transmitted the data is safe as well as secure. This practice is a must keeping in mind the existing as well as evolving cyberthreats. Information irrespective of being stored or in transit can be effectively protected by leveraging end-to-end encryption with the twin goals of security as well as full privacy.
3. Robust Multi-Factor Authentication (MFA)
Today one cannot depend on passwords alone for ensuring security. Along with passwords, security codes sent to smartphones as well as biometrics are used to ensure foolproof security. Note that biometrics are unique to the user. Examples being fingerprints, facial recognition as well as voice recognition. Security codes are sent to devices in the possession of the user. So even if the password is compromised there are two additional layers of security.
4. Using AI (Artificial Intelligence) for Threat Detection
AI solutions track network traffic as well as learn from earlier attacks. The former finds out and halts threats in minimum time. Machine learning as well as deep learning algorithms are used to detect cyberthreats. AI finds out anomalies that may be missed by humans. Also, AI technology is far faster than human operators. AI predicts future threats and prevents them from causing major damage.
5. Employing Zero Trust Architecture
Each user, device as well as application is not automatically trusted. They must provide credentials to be trusted. This limits data breaches, hacking as well as unauthorized access. Each entity is vetted before granting permission to access resources.This is indeed a highly effective strategy against cyberattacks.
<h1>Other Important Cybersecurity Measures</h1>
<ul>
<li>The Importance of Educating your Employees</li>
</ul>
Many cyberattacks make use of phishing and social engineering. It is essential to educate your employees on cybersecurity best practices. Many use weak passwords, easily guessable practices or use the same password across different accounts. They should be educated about the importance of using strong and complex passwords. A strong password contains both uppercase and lowercase letters, numbers as well as special characters. Despite taking precautions, breaches may occur. You should have a plan ready for what to do in the event of a breach. All employees should be trained on what to do in the event of a breach. This way you can limit the damage caused by the breach. 
Cyberattack awareness must be included during the employee onboarding process. Regular training should be provided on the existing cyber threats and how to identify and mitigate them. If an employee is working from home his/her device should have the latest antivirus software. They should enable automated software updates to their systems that patch vulnerabilities. If possible, encourage remote workers to use a VPN (Virtual Private Network). The latter is encrypted thus preventing hackers from accessing confidential data. 
<ul>
<li>Disaster Recovery and Data Backup</li>
</ul>
Regularly backup your data. So even if your data is compromised in case of a cyberattack you can get your data back. Your data is not lost permanently. Make sure you take multiple copies of your data and keep the copies in different locations. Disaster recovery is another important aspect.  Even with robust cybersecurity measures in place data breaches can occur. You need to have a realistic and effective plan for backup as well as disaster recovery. This way organizations quickly to disruptions from hacking or data breaches. Downtime can result in financial losses as well as reputational damage. Efficient data backup as well as disaster recovery processes can prevent or limit the damage caused to the organization.
<ul>
<li>Email Security</li>
</ul>
Phishing is still a major factor in data breaches. Your staff can be tricked into clicking on links or disclosing classified data. Malicious emails should be stopped from reaching your business's inboxes. Use firewalls to prevent attacks on your organization’s network. It is necessary to have a robust security culture in your organization. Invest in relevant training as well as encourage open communication among your staff.
<ul>
<li>Continuous Monitoring Strategy</li>
</ul>
By tracking system logs as well as user activities organizations can trace unusual behavior and take steps in real time to tackle the threat. This is a proactive strategy implemented to ensure minimum damage. Security Information and Event Management (SIEM) solutions automate tracking as well as analyzing security related data. The former also enhances compliance reports. SIEM systems gather as well as correlate data across the organization. Real-time alerts as well as comprehensive reports are generated.
<ul>
<li>Proactive Approach</li>
</ul>
It is important to have a proactive approach rather than a reactive approach when it comes to cybersecurity. Have cybersecurity experts carry out regular audits of your organization’s security framework. This will reveal vulnerabilities in the system. Next fix the vulnerabilities at the earliest. It is very important to have a quality and latest version of antivirus software. Have firewalls installed to filter out malicious traffic. Inform the employees on the latest malware, phishing attacks as well as ransomware attacks. Each and every password should be strong and complex. It is important to change passwords regularly as a precautionary measure. Employees should be instructed to not use unsecured Wi-Fi networks as this could compromise your organization.. 
<ul>
<li>Compliance with Existing Regulations</li>
</ul>
Cybersecurity regulations in different industries must be adhered to strengthen the cybersecurity level of your organization. Not doing so can lead to hefty fines or data breaches.
<ul>
<li>Implement Advanced Endpoint Protection (AEP)</li>
</ul>
Endpoints include IoT (Internet of Things) devices, smartphones, tablets as well as laptops. These are the entry places for malicious actors. Any breach can incapacitate the whole organization. Invest in Advanced Endpoint Protection solutions to secure the safety and functioning of your organization. The investment is worth the ROI (Return on Investment) in terms of finance, peace of mind as well as brand image.
<ul>
<li>Robust Cloud Security</li>
</ul>
Nowadays more and more organizations are adopting the cloud. There should be monitoring round the clock to detect unauthorized access. All data should be encrypted well.
<h1>Future Cybersecurity Trends</h1>
There will be an increase in the usage of deception technology. Set traps for hackers to detect and prevent hacking attempts in real time. Biometrics will replace passwords for authentication. The former is much more effective as well as reliable to prevent unauthorized access. Quantum cryptography will become essential in best cybersecurity practices. Businesses of different sizes and types will increasingly leverage quantum cryptography to block cyberthreats. AI solutions have the ability to go through a large amount of data to identify suspicious activity as well as threats. The former does it in real-time which cannot be matched by human operators. AI can also predict future threats and provide the solution to combat them successfully. It is imperative to take steps to address future cybersecurity challenges for businesses in 2025,
It is necessary to introduce cybersecurity best practices at the earliest. Several organizations do not have the in-house capability to do so. Using the services of a cybersecurity provider will protect your organization from cyberthreats. The provider will carry out important activities such as risk assessment, vulnerability management, effective incident response as well as regulatory compliance. Look out for cost-effective cybersecurity solutions for small businesses in 2025.
<a href="https://bit.ly/4eqL8UF">CoffeeBeans </a> specializes in advanced cybersecurity measures for enterprises in 2025. The company has the requisite expertise and experience in implementing effective and efficient cybersecurity solutions. We offer stellar quality at competitive rates. Our clients can vouch for our transparency, real-time communication as well as adherence to stipulated deadlines.  Reach out to us at  <a href="mailto:enquiries@coffeebeans.io">enquiries@coffeebeans.io</a> to know how we can meet your unique and specific goals and objectives.

Cyberattacks are becoming more frequent and sophisticated nowadays. There are phishing attacks that trick individuals into revealing confidential information. AI(Artificial Intelligence)  is used by hackers to mount relatively more sophisticated attacks.

Top 5 Cybersecurity Measures for Businesses in 2025

Most organizations in the world have adopted cloud technology. As per Gartner a significant 85% of organizations will opt for a cloud-first approach by 2025. The cloud computing market size is predicted to grow, increasing 2.5% in 2024 to reach a total of $675.4 billion. The Covid 19 pandemic led to many companies unable to access their on-premises infrastructure. Also, the pandemic saw the rise of remote working. <a href="https://coffeebeans.io/blogs/the-future-of-enterprise-cloud-technology-5-trends-to-watch-out-for-in-2024">Cloud technology</a> enables anytime anywhere access and collaboration. It made remote work easy and convenient.
<img src="https://media.coffeebeans.io/Add%20a%20subheading%20(2).png" alt="">
<h1>What is Cloud Computing?</h1>
Resources such as hardware, software, servers, network, databases, and others are hosted on a remote site by third party providers. Users do not have to buy the whole resource they want. They only pay for what they use. All the user needs to access the cloud service is a web browser and an Internet connection. The leading public cloud platforms today are AWS (Amazon Web Services), Microsoft Azure and GCP (Google Cloud Platform).
<h2>What is Public Cloud?</h2>
This is the most popular type of cloud deployment. Cloud services are provided by third party providers. The latter own, operate and manage the resources such as hardware, software, data storage and others. Users access the resources through a public Internet connection and a web browser. The resources are shared by many entities.
<h2>Advantages of Public Cloud</h2>
<ul>
<li>Saving Money</li>
</ul>
Organizations do not have to purchase hardware, software or other resources. Also, office space is not required to keep the resources. So, a lot of money is saved. While using a public cloud you only pay for what you use. So, there is no wasting on resources not used.
<ul>
<li>No Responsibility of Maintenance</li>
</ul>
Even employees with little or no technical knowledge can use the<a href="https://coffeebeans.io/blogs/why-is-it-so-important-to-have-managed-services-and-cloud-solutions"> cloud</a>. Any updates or necessary upgrades are the responsibility of the cloud provider. If there are problems with the resources they are taken care of by the cloud provider.
<ul>
<li>No Need of In-house Team</li>
</ul>
Usually organizations need an in-house IT team to manage their resources. Thanks to the cloud organizations don’t require a full time in-house IT team. So you don’t have to hire and train people to take care of your on-premises infrastructure. You save money in terms of salaries as well as recruitment and training costs.
<ul>
<li>Superior Scalability</li>
</ul>
Organizations often experience fluctuating demands. Thanks to public cloud companies can scale up or down the usage of resources by a few clicks. This is not possible in companies having on-premises infrastructure. In case of high demand, buying and configuring resources takes a lot of time and money. If there is less demand the resource will be unused and occupy office space.
<ul>
<li>Disaster Recovery</li>
</ul>
In case of natural or manmade disasters important data can be lost. This leads to downtime which in turn causes financial and reputational damage. In the public cloud model backups of the data are taken and stored in multiple data centers across the world. So even if one data center is down your data and applications are safe. This advantage cannot be matched by on-premises infrastructure.
<h2>Drawbacks of Public Cloud</h2>
<ul>
<li>Security</li>
</ul>
Multiple organizations share the hardware and software of the cloud. So, your data runs the risk of being compromised.
<ul>
<li>Compliance</li>
</ul>
Certain regulations require the company data to be stored on-premises. This is not possible in the public cloud model.
<h1>When to Use the Public Cloud?</h1>
The public cloud should be used in the following conditions
<ul>
<li>Software coding and testing scenarios</li>
<li>Requirement for extra resources during spike in demands</li>
<li>Implementation of IT (Information Technology) as well as enterprise processes</li>
<li>When requirements are predictable</li>
</ul>
<h1>What is Private Cloud?</h1>
This type of cloud model is dedicated exclusively to a single organization. The private cloud may be present in your organization’s on-site datacenter or at a remote third party cloud provider.
<h2>Advantages of Private Cloud</h2>
<ul>
<li>Superior Security</li>
</ul>
Only your organization’s data is present in the private cloud. Data confidentiality is assured. There are less chances of unauthorized access compared to the public cloud model.
<ul>
<li>Superior Compliance</li>
</ul>
In highly regulated sectors such as finance and healthcare certain countries and rules require that the data be present on-premises.  This requirement is fulfilled by the private cloud.
<ul>
<li>Ease of Customization</li>
</ul>
Organizations can customize the private cloud environment as per their unique as well as specific business needs.
<ul>
<li>Superior Performance</li>
</ul>
This model has higher performance as well as efficiency compared to the public cloud model.
<ul>
<li>Superior Control</li>
</ul>
Organizations using private clouds have the ability to determine the cloud's configuration. In the public cloud model the cloud service provider manages all the configurations.
<h2>Drawbacks of Private Cloud</h2>
<ul>
<li>Difficult Scalability</li>
</ul>
If the organization’s demands increase then procuring and configuring additional resources takes time.
<ul>
<li>Dependence on In-house team</li>
</ul>
The organization has to operate, manage and maintain the infrastructure. The former needs a dedicated team to do these roles.
<h1>When to Use the Private Cloud?</h1>
Select the private cloud for the below scenarios
<ul>
<li>Entities capable of acquiring high performance technologies</li>
<li>Big businesses that need sophisticated data center technologies</li>
<li>Organizations desiring high level of control as well as security </li>
<li>Government entities and sectors with many regulations.</li>
</ul>
<h1>What is Hybrid Cloud?</h1>
This cloud model includes both public cloud deployment and private cloud deployment. Certain data and workloads are hosted on the private cloud and the remaining on the public cloud. Generally, sensitive data and workloads are put on the private cloud. The remaining non-sensitive data and workloads are put on the public cloud. Hybrid clouds permit applications as well as data to move between disparate environments.
<h2>Advantages of Hybrid Cloud</h2>
<ul>
<li>Obtain the Benefits of Both Public Cloud and Private Cloud Models</li>
</ul>
Organizations get the ease of scalability as well as cost savings of public cloud. They also get the superior compliance, superior control, and ease of customization of private clouds.
<h2>Drawbacks of Hybrid Cloud</h2>
<ul>
<li>Burden of Management</li>
</ul>
There is more management effort as you have to switch between private cloud and public cloud.
<ul>
<li>Integration Issues</li>
</ul>
Integrating public and private clouds can be challenging. Organizations may face compatibility problems between disparate cloud environments.
<ul>
<li>Security Aspect</li>
</ul>
Data may need to be transferred between clouds posing the risk of security breaches.
<h1>When to Use the Hybrid Cloud?</h1>
The hybrid cloud is suitable for the following scenarios.
<ul>
<li>Wanting to leverage  the top cloud platform(s) in the market which may vary with time</li>
<li>Ramping up security provided by existing cloud solutions</li>
<li>Strategically invest in cloud without tradeoff on the service quality of public or private cloud offerings</li>
<li>Entities operating in different domains with unique performance, compliance as well as security demands.</li>
</ul>
<h1>Deciding between Public, Private and Hybrid cloud computing</h1>
Organizations need to craft a well honed cloud strategy that fully leverages each cloud environment. Firstly outline the demands of the organization's workloads. Next based on the benefits as well as drawbacks of the models, arrange them in order of importance.
The shortlisting of a specific type of cloud model is based on future growth expected, IT resources, money willing to be spent by your organization as well as required applications of the cloud solution. Note that your organization's demands may change in time and so will be the best cloud type.  
<h1>Cloud responsibility: A shared model</h1>
Don't expect your job is complete by identifying the cloud type. Due diligence is mandatory to mitigate wrong choices. It is referred to as a shared model of cloud responsibility. While the respective cloud providers will take care of the infrastructure your company has to handle
<ol>
<li>Disaster recovery strategy</li>
<li>Cloud related security as well as encryption</li>
<li>Defining Access Levels for different members of your organization </li>
</ol>
<h1>Cloud Migration Strategies</h1>
Now we go through the processes to migrate your data and applications from on-premises to a cloud environment.
<h1>The 7Rs of Cloud Migration</h1>
<ul>
<li>Retire</li>
</ul>
Certain workloads are obsolete or no longer needed. They are let go.
<ul>
<li>Retain</li>
</ul>
These are useful applications not ready for cloud migration. Organizations can keep them on-premises and consider migrating them sometime in the future.
<ul>
<li>Relocate</li>
</ul>
This migration strategy is used when the cloud environment is similar to the on-premises environment. Here, virtual machines are migrated to the cloud utilizing hypervisors.
<ul>
<li>Rehost</li>
</ul>
Here, applications are just placed in the cloud from on-premises without making any changes. This procedure is also referred to as 'Lift and Shift'.
<ul>
<li>Repurchase</li>
</ul>
Here the existing application is let go and in its place a cloud-based application is acquired.
<ul>
<li>Re-platform</li>
</ul>
Applications are changed to take advantage of cloud features and then moved to the cloud.
<ul>
<li>Refactor</li>
</ul>
The application's architecture is completely changed. Significant time and resources are spent on this effort. After that it is moved to the cloud.
<h1>Further Migration Steps</h1>
The next step in the migration is to decide whether to migrate to public, private or hybrid cloud. Then organizations decide on the type of cloud services to be provided by the shortlisted cloud model.
Below are the different stages of the migration process
<ul>
<li>Identify Business Model</li>
<li>Outline the Migration Strategy</li>
<li>Calculate the costs</li>
<li>Shortlist Cloud Type</li>
<li>Create the Architecture</li>
<li>Carry out the necessary steps of migration</li>
</ul>
A quality cloud migration strategy mitigates risks, brings down downtime as well as results in a smooth transition.
<ul>
<li>Meticulous Planning</li>
</ul>
Be clear about the organization's business goals. Examine existing IT infrastructure, applications as well as data. Consider the associated expenditure, probable risks as well as various dependencies. Use different cloud providers to avoid vendor lock-in.
<ul>
<li>Seamless Implementation</li>
</ul>
First test the migration plan to find out whether it works. Test whether the applications and data function satisfactorily in the new cloud environment. Also test for performance level and security aspects. Data migration should be meticulously planned as well as executed.
<ul>
<li>Maintenance Aspect</li>
</ul>
Have a disaster recovery plan in place. It is important to select the suitable migration tools keeping in mind the company's goals and objectives. Use automation wherever needed in place of manual processes. Establish security policies to ensure the security and confidentiality of data. It is important to have a plan such that cloud spending does not go overboard over time. Monitor usage and be ready with cost-saving steps.
Note that the migration process is not simple. It is recommended to get external help if you lack the expertise and experience in migration. The migration strategy should have clear procedures to do rollbacks in the event of any problems.
With its proficiency in seamless cloud migration, CoffeeBeans is poised to implement your organizations cloud migration requirements. Our migration strategies have evolved and perfected over time. Consult us for best cloud migration strategies for hybrid solutions. We offer complete transparency and real-time communication to our clients. As we are based in India, we offer competitive rates as well as stellar quality. CoffeeBeans is committed to keeping your organization's data, applications, and business process confidential. We also devise cloud migration strategies for small businesses choosing hybrid solutions. Reach out to us at <a href="mailto:enquiries@coffeebeans.io">enquiries@coffeebeans.io</a> to know how we can help meet your specific and unique requirements.

Most organizations in the world have adopted cloud technology. As per Gartner a significant 85% of organizations will opt for a cloud-first approach by 2025. The cloud computing market size is predicted to grow, increasing 2.5% in 2024 to reach a total of $675.4 billion. The Covid 19 pandemic led to many companies unable to access their on-premises infrastructure. Also, the pandemic saw the rise of remote working. Cloud technology enables anytime anywhere access and collaboration. It made remote work easy and convenient.

Cloud Migration Strategies: Choosing Between Public, Private, and Hybrid Solutions

Did you know that everyday 2.5 quintillion bytes of data are produced? Today almost half of the world's adult population avails of digital banking. As per Markets and Markets the size of the global <a href="https://bit.ly/4dGqakl">big data</a> market will increase from $138.9 billion in 2020 to $229.4 billion by 2025 at a CAGR of 10.6%. Customer interactions, financial transactions and other forms of activity generate a lot of data in banks. The existing systems and processes are unable to handle the volume, velocity, and variety of data. Big data is the solution to this challenge faced by banks.  Thus, banks across the world are looking at leveraging the latest big data technologies to remain relevant and competitive in the market. The worldwide big data analytics in banking market was worth $307.52 billion in 2023 and projected to be worth $745.16 billion by 2030. The market will grow at a CAGR of 13.5% from 2024 to 2030.
<h1>What is Big Data?</h1>
Big data refers to large and varied data that is difficult to handle and analyze using conventional methods. The data arrives at great speed and in a variety of formats. The 4 V’s of big data are as follows. 
<ul>
<li>Volume</li>
</ul>
This refers to the huge quantities of data. That can range from terabytes to petabytes.
<ul>
<li>Velocity</li>
</ul>
This is the rate at which data flows in and requires real-time processing
<ul>
<li>Variety</li>
</ul>
This is the different forms of data. The latter can be in the form of text, image, audio, or video.
<ul>
<li>Veracity</li>
</ul>
This refers to the accuracy as well as reliability of data. 
<h1>How Big Data is Changing Banking</h1>
<ul>
<li>Analyzing Stock Prices</li>
</ul>
Big data takes into account different parameters such as human resources, impact on environment as well as social reputation. Thus, banks are able to make better investment choices.
<ul>
<li>Superior Customer Service</li>
</ul>
Virtual assistants using AI are instances of the role of big data in banking. The former take care of client queries as well as give timely reminders of important dates.
<ul>
<li>Superior Lending Process</li>
</ul>
Certain banks employ big data along with social media to find out the credit risk of giving loans to applicants. Thanks to big data analytics banks can make superior lending decisions.
<ul>
<li>Superior Employee Productivity</li>
</ul>
Certain banks use big data tools to track performance metrics such as employee productivity as well as metrics such as client acquisition as well as retention.
<ul>
<li>Superior Cybersecurity</li>
</ul>
Big data solutions trace out fraud as well as avert internal risks. Banks use big data technology to prevent fraud in real-time.
<ul>
<li>Automation of Business Processes</li>
</ul>
Big data solutions are estimated to have the ability to automate up to 30% of bank operations. Automation brings down risk of employee error as well as saves a lot of money.
<ul>
<li>Superior Management of Fraud</li>
</ul>
Big data solutions track client spending and are able to trace unusual types of spending thus averting unauthorized transactions. The former is also useful in cases of identity fraud. By using big data solutions, the banking industry is able to ramp up security levels.
<ul>
<li>Leverage Client Feedback</li>
</ul>
Big data solutions go through client feedback and segregate queries, comments as well as concerns. The former empowers banks to address client feedback promptly driving up trust as well as loyalty among clients.
<ul>
<li>Client Segmentation</li>
</ul>
This step is important for targeted marketing campaigns. Big data categorizes clients into different segments based on factors such as income, risk appetite, credit card usage, demographics, and others. As such they can offer products and services tailored to each segment and which is closer to their demands as well as expectations.
<ul>
<li>Hyper-Personalization</li>
</ul>
Big data analyzes client data for factors such as financial position, how they shop, how they invest and others. This enables banks to target each client with personalized offerings. As a result, client satisfaction is ramped up. Big data also helps banks predict client churn and thus take steps to prevent that from happening.
<h1>Implementation of Big Data in Banks</h1>
<ul>
<li>Identify Goals</li>
</ul>
Be clear about what the bank desires to attain by using big data. Goals could range from ramping up efficiency, preventing frauds or superior client experience.
<ul>
<li>Create a Roadmap</li>
</ul>
 This should list out the steps to implement big data adoption and integrate big data with the bank's existing systems.
<ul>
<li>Acquire Data Storage and Processing Capacity</li>
</ul>
Have scalable storage infrastructure to handle the enormous amount of data coming from banks. Buy powerful computing resources able to analyze as well as process large amounts of data. Use cloud platforms to manage the available data.
<ul>
<li>Consolidation of Data</li>
</ul>
Integrate data coming from different sources to a single platform. This includes third-party data as well as data coming from social media.
<ul>
<li>Select the Best Tools</li>
</ul>
Select a big analytics tool which is high performing as well as compatible with the bank's present infrastructure. The tool should be user friendly and be able to be used by employees with little or no technical knowledge.
<ul>
<li>Recruiting and Training</li>
</ul>
Be aware of the skills required to use big data analytics. Next assess the knowledge of the bank's human resources. Identify skill gaps that need to be bridged. Organize training to get the staff well versed and up-to-date with the latest big data tools, technologies, and methodologies. If necessary, recruit data scientists as well as data engineers with relevant experience as well as expertise.
<ul>
<li>Incorporating Feedback</li>
</ul>
Constantly monitor the performance level of big data solutions. The latter provides insights which should be incorporated to the bank's day to day operations. Upgrades and updates in the domain of big data analytics should be implemented so that the bank is up-to-date as well as retains the competitive edge in the constantly evolving market.
<h1>Challenges of Banking Resolved by Big Data</h1>
<ul>
<li>Data Assuming Larger Proportions</li>
</ul>
Many banks are already struggling to store, manage, and analyze their existing data. With time the amount of data is getting bigger and unmanageable. One fundamental challenge is to separate the useful data from the useless.
<ul>
<li>Increasing Need of Regulatory Compliance</li>
</ul>
Wherever there is data there are associated risks. A top priority of banks is to keep their data safe. Cybersecurity measures are critical given the increasing incidents and sophistication of cyber-attacks. There is an increasing number of regulations related to the storage and use of banking data.
<ul>
<li>Legacy Systems</li>
</ul>
This type of banking infrastructure was not designed to handle the present size and complexity of data. If not replaced they can make a bank obsolete or uncompetitive. They must be upgraded or replaced. Only big data technologies have the capability to handle modern day banking systems as well as processes.
<h1>Examples of Banks Leveraging Big Data</h1>
<ul>
<li>Goldman Sachs</li>
</ul>
This is a major global investment bank. The bank is an early adopter of big data to deliver superior client service, be competitive as well as trace good investment opportunities. Thanks to big data tools the bank is able to interpret data from multiple formats.
<ul>
<li>JP Morgan &#x26; Chase </li>
</ul>
The prestigious bank offers a plethora of services such as investment banking and private banking. The former harnesses big data to go through data of client transactions and merge them with other data to determine the creditworthiness of clients. Thanks to cutting-edge big data technologies they are able to identify top purchasers of financial products and services.
<h1>Pros of Using Big Data in Banks</h1>
<ul>
<li>Become More Competitive</li>
</ul>
Thanks to big data, banks have the ability to make data-driven decisions. Banking institutions get valuable knowledge related to risk factors as well as market trends. The former are thus able to make superior lending as well as investment choices.
<ul>
<li>Superior Regulatory Compliance</li>
</ul>
Big data automates data gathering, analysis as well as reporting. This decreases the incidents of non-compliance and having to dole out hefty penalties. Big data makes consolidated as well as detailed transaction records that simplify the process of proving compliance in case of audits.
<ul>
<li>Ramp up Efficiency</li>
</ul>
Big data can trace unprofitable bank branches, services as well as products upon which the bank can terminate them. The former's automation of banking-related tasks frees staff to focus on more important tasks. As a result, banks are able to raise productivity and efficiency giving them a significant edge over the competition.
<ul>
<li>Superior Client Experience</li>
</ul>
Big data is able to better identify client needs as well as preferences. The bank is able to use these insights to offer each client products and/or services that meet their goals and objectives. This makes the bank’s clients feel valued as well as appreciated. This translates to boosting of client trust as well as loyalty. Banks are also able to upsell as well as cross-sell their existing products and/or services. It is far easier to retain existing clients than acquiring new ones. By leveraging big data, clients retain existing clients as well as are able to onboard new ones. This translates to increased revenues as well as profit margins for the bank.
<ul>
<li>Superior Loan Approval</li>
</ul>
Traditional methods used only credit scores to assess creditworthiness and decide whether to grant a loan or not. Big data uses multiple factors such as income, social media, nature, and volume of financial transactions to determine loan eligibility. The technology also reviews relevant documents in real time so that the loan approval process takes far more time than it used to take before big data usage. As a result, there is reduced risk of loan default and increased profitability for the concerned bank.
<h1>The Future of Big Data in Banks</h1>
Big data technology is continuously evolving and improving. The increasing trust in this state-of-the-art technology will result in adoption of big data by more banks. AI (Artificial Intelligence) and big data will be used together to enhance banking systems and processes. Technologies such as <a href="https://coffeebeans.io/blogs/building-the-next-gen-supply-chain-with-blockchain-technology">Blockchain</a> will be used to ensure superior levels of security. Big data will drive up sustainability and keep the environment in mind. The technology will enable banks to serve unserved communities such as those living in remote areas. Big data will enable banks to go from local presence to a global one.
Now that you know how big data is changing the banking industry, it is time to take appropriate action. With expertise in both Big Data and banking, CoffeeBeans is well poised to integrate Big Data technology with the existing infrastructure of your bank. We have a pool of talented professionals who have relevant experience in implementing Big Data in banks. As such they are aware of the challenges in the process and will speedily resolve them. Reach out to us at <a href="mailto:enquiries@coffeebeans.io">enquiries@coffeebeans.io</a> to know how we can help you attain your unique and specific needs and expectations.

Did you know that everyday 2.5 quintillion bytes of data are produced? Today almost half of the world's adult population avails of digital banking. As per Markets and Markets the size of the global big data market will increase from $138.9 billion in 2020 to $229.4 billion by 2025 at a CAGR of 10.6%.

Big Data Analytics in Banking: Transforming Financial Services

<h1>Introduction</h1>
Many organizations are familiar with the term ‘<a href="https://bit.ly/3XQbhqT">DevOps</a>’. Some organizations may be implementing the same. The significant benefits of DevOps have been experienced by many organizations. But did you know that there is a new set of practices that takes DevOps to another level? That new paradigm is GitOps which is used along with DevOps for synergy. Consequently, many organizations are using DevOps along with GitOps. As per TechSurvey, 15% of organizations worldwide adopted GitOps in 2021. The company  predicts that 60% of organizations globally will adopt GitOps in 2024.
<h1>Key Insights</h1>
In this article you will explore the basic concepts of DevOps and GitOps. You will also get acquainted with their benefits. Next, we’ll explore the implementation of GitOps for business operations. We look at examples of actual companies embracing GitOps in 2024. Finally you will come to know how GitOps is used along with DevOps for more benefits. 
<img src="https://media.coffeebeans.io/How%20GitOps%20is%20Revolutionizing%20DevOps%20Practices%20in%202024.png" alt="">
<h1>Definition of DevOps</h1>
DevOps is a different approach to the<a href="https://bit.ly/4gwPtrD"> software</a> development cycle. Earlier the software development team and IT operations used to work separately. In DevOps the development as well as IT operations staff do not work in silos. There is communication and collaboration between them right from the start.
Prototypes are quickly built and presented to stakeholders for feedback. The feedback is incorporated into the prototype. These steps are repeated until the full product is built. In DevOps there are quicker as well as relatively more frequent releases. Repetitive tasks are automated. So are manual processes. Errors are reduced and the delivery time slashed.
The different elements of DevOps include continuous integration, continuous delivery as well as automation.
<h1>Benefits of DevOps</h1>
<ul>
<li>More Flexibility</li>
</ul>
DevOps involves continuous delivery as well as testing. Fresh functionality can be quickly introduced in the market.
<ul>
<li>Short Development Time</li>
</ul>
Since there is collaboration, all key individuals are there in the loop. You do not have to wait for weeks/months to release a particular product.
<ul>
<li>Superior Security</li>
</ul>
Traditionally testing is done post software development. In DevOps, testing is done early which either prevents errors completely or catches bugs before their impact increases. 
<ul>
<li>Superior Software Quality</li>
</ul>
Since flaws as well as vulnerabilities are detected earlier and removed, the final deliverable is of relatively better quality. The latter determines the success of the software product in the intensely competitive market.
<ul>
<li>Better Communication</li>
</ul>
Communication takes place early and throughout the process. This removes misunderstandings and delays. The result is a high-quality deliverable facilitated by consensus between all stakeholders.
<ul>
<li>Leverages Automation</li>
</ul>
Manual processes take time and run the risk of human error. Repetitive processes are replaced by automation saving both time and effort.
<ul>
<li>Saves Money</li>
</ul>
Faster time to market ensures quick realization of revenue and profits. Catching bugs early saves money. Remove bugs late in the development process costs more time and money than done early. Automation is cheaper than having human resources do the same job. There is clarity about the product/service at the very beginning preventing a deliverable that is different from the stakeholder expectations. Time and effort go in changing the flawed deliverable. This scenario is prevented by adopting DevOps.
<h1>Definition of GitOps</h1>
GitOps is a new approach to infrastructure management. It borrows DevOps best practices and uses them for infrastructure automation.
Git repositories play the role as the primary source of truth for infrastructure as well as application states. Pull requests are used to implement system changes.
<h1>Benefits of GitOps</h1>
<ul>
<li>Ensures Consistency as well as Compliance</li>
</ul>
In GitOps changes are automatically applied. So, the infrastructure is consistent with the desired state. This step also ensures that the system is compliant with existing industry standards as well as regulations.
<ul>
<li>Ease of Scalability</li>
</ul>
Deployment procedures are automated. So complex systems can be managed better. This step makes scalability simple.
<ul>
<li>Superior Reliability</li>
</ul>
There exists the facility to revert to an earlier state in case of any problem. This brings in more reliability as well as less recovery time.
<ul>
<li>Superior Security</li>
</ul>
Since the source of truth is maintained in a central authority, security is ensured via auditable change logs as well as mandatory code reviews. The possibility of unauthorized changes is made difficult. Thus, overall security is enhanced.
<ul>
<li>Ease of Auditing</li>
</ul>
It is usually overwhelming to perform auditing with the help of manual tools. GitOps offers a better alternative for auditing. You just have to view the Git logs. By doing that you come to know who did what, when and for what purpose. Also, there is clarity on the impact on deployment.
<ul>
<li>Ease of Experimentation</li>
</ul>
Development teams are afraid of experimenting in case something goes wrong and cannot be fixed. In the GitOps model you have access to all the changes made to the environment. So, in case of problems, it is simple and convenient to revert to the earlier state.
<ul>
<li>Superior Efficiency</li>
</ul>
You can roll back changes in the Git tool if fresh changes do not act as they were supposed to be. The team does not have to toggle between tools in order to manage changes to the relevant software environment. All things occur in the sole version control tool used to craft the deliverable.
<ul>
<li>Already Popular and Widely Used</li>
</ul>
Git is the numero uno version control system. There is a significant number of programmers with knowledge of Git. So, no training is necessary and you can easily get competent programmers. Time is saved and your project can commence almost immediately.
<h1>Implementation of GitOps</h1>
Note that GitOps cannot be classified as a tool. The former refers to a package of practices that can be used in different situations.  General steps to implement GitOps are as below.
<ul>
<li>Make the Git Repository Ready</li>
</ul>
 This is the place to store the infrastructure as well as application configuration files. A suitable Git hosting service for instance GitHub can be utilized.
<ul>
<li>Configuration of Deployment Scripts </li>
</ul>
The latter outline the way applications as well as infrastructure are deployed to the target environments. A configuration management tool is required for this purpose.
<ul>
<li>Make a GitOps Tool Ready</li>
</ul>
This step is to monitor the Git repository. Deployment scripts are activated whenever changes are made. Employ a GitOps tool offering support to the infrastructure as well as platform. For instance, Flux or ArgoCD can be employed.
<ul>
<li>Final Deployment</li>
</ul>
The application can be deployed by making changes to the configuration files inside Git and by introducing pull requests. The GitOps tool will automatically sync the changes with the target environments.
<h1>How GitOps Makes DevOps Better</h1>
We take a look at why GitOps is the future of DevOps. GitOps enhances order as well control in DevOps. The result is making entities more efficient in their operations, promoting accuracy as well as compliance with relevant laws. In Short, GitOps modernizes DevOps. Software development as well as infrastructure management have greater control, efficiency as well as scalability thanks to GitOps. Deployment processes become transparent, automated, and collaborative. The system is made more efficient and manual intervention is minimized. GitOps results in the system consistently matching with the target configurations.
GitOps is based on Git, an open-source version control system. More and more organizations are adopting GitOps. The latter uses popular tools such as version control to manage infrastructure-as-code (IaC) as it manages application code. The result is a shorter timespan to react to client demands as well as market needs.
GitOps makes all deployments automated. It efficiently as well as effectively manages the existing infrastructure. There is ramped up collaboration between the development as well as operations team facilitated by GitOps. By adopting the latter, entities get instant solutions to client feedback improving the overall deliverable.
GitOps is compatible with Kubernetes as well as more container tools. To employ Kubernetes, deployment manifests have to be stored in Git repositories. Processes such as fault tolerance, load balancing as well as scaling are automated. Programmers can focus on software programming as well as give more value via the deliverable. The former do not have the responsibility of managing the infrastructure.
Agile software development teams benefit from a quick workflow thanks to adopting GitOps. Other advantages include superior collaboration, valuable feedback as well as iterations in relatively lesser time. Git assumes the responsibility and storage for codes as well as configurations. Software development teams can do seamless version control as well as monitor any changes made. If the situation demands changes can be rolled back with ease. Deadlines are met better and enhanced quality of the deliverable is achieved.
DevOps delivers improved performance as well as results. Adding GitOps to the former makes software development teams have a significant edge over the competition in terms of automation, managing infrastructure, performing auditing as well as ensuring superior security. The last advantage is pertinent given the increased frequency as well as sophistication of cyber threats.
<h1>How Companies are Implementing DevOps in 2024</h1>
<ul>
<li>IBM is using GitOps in its cloud-native as well as Kubernetes operations. As a result IBM's development and operations teams are experiencing better productivity. By using Argo CD which is a GitOps tool, the deployment process is quicker and has higher reliability. Adopting GitOps is enhancing automation for the development teams of IBM. </li>
<li>Shopify, a famous e-commerce company, has embraced GitOps practices to better manage the company's Kubernetes infrastructure. The use of a certain GitOps tool namely Flux ensures automation of deployments as well as efficient environment management. Other benefits include minimal configuration drift along with seamless collaboration between the development unit and operations unit.</li>
</ul>
<h1>GitOps Trends to Look Forward in 2024</h1>
In 2024, GitOps is supporting multi-cloud as well as hybrid environments. The platform features robust support for edge computing too. Fresh and potent tools are being introduced to make the implementation of GitOps more simple and convenient.
At Coffeebeans, we specialize in GitOps and DevOps integration and employ our skills to provide high quality solutions for organizations. The company has a talented team of professionals who are adept at combining GitOps and DevOps for superior results. Note that our developers are proficient with the Git version control system. We are skilled at overcoming the challenges and understanding the nuances of integrating DevOps and GitOps in the process of producing high quality software. As we are based in India, our rates are competitive. Over time we have established a mature outsourcing process characterized by high transparency and quick response to any doubts or issues. Our clients can vouch for our adherence to agreed deadlines, stellar quality, high performance as well as adaptability. 
We sign an SLA (Service Level Agreement) at the onset so you know our response timeframe. As many clients are particular about protecting business secrets, we are open to sign an NDA (Non-Disclosure Agreement). Reach out to us at <a href="mailto:usenquiries@coffeebeans.io">enquiries@coffeebeans.io</a>  to know how best we can serve you. CoffeeBeans looks forward to being a reliable and trusted partner in reaching your unique as well as specific goals and objectives. Join the GitOps revolution in 2024 to take your organization to another level. We will facilitate GitOps implementation in DevOps for you with precision and efficiency. Enhancing DevOps with GitOps will ramp your organization’s productivity as well as performance.

In this article you will explore the basic concepts of DevOps and GitOps. You will also get acquainted with their benefits. Next, we’ll explore the implementation of GitOps for business operations. We look at examples of actual companies embracing GitOps in 2024. Finally you will come to know how GitOps is used along with DevOps for more benefits

How GitOps is Revolutionizing DevOps Practices in 2024

As per Curate Labs as of 2024, there exist roughly 2 billion websites online, that includes 1.13 billion on the World Wide Web. The question in everybody's mind is what to expect in web development in 2024. We look at how web development is evolving in 2024.
<img src="https://media.coffeebeans.io/The%20Future%20of%20Web%20Development.png" alt="">
<h1>Web Development Trends to Watch in 2024 and Beyond</h1>
<ul>
<li>The Rise in Popularity of TypeScript</li>
</ul>
Did you know that currently in open-source development TypeScript is used more than Java? In fact, it ranks as the 3rd most used programming language for open-source development. Advantages of TypeScript are superior productivity as well as superior error detection capabilities.
<ul>
<li>Dark Mode</li>
</ul>
This particular feature was popular in the past and has become more popular today. Dark mode refers to a low-light UI with a dark color being the primary background color. Prominent organizations such as Google, Apple and WhatsApp were early in using dark-mode interfaces leading others to do the same. The color black lends style and sophistication when used in websites. The benefits of dark mode include reduced eye strain including in low light environments as well as extended battery life of devices.
<ul>
<li>Jamstack Architecture</li>
</ul>
Note that this architecture dates back to 2015. Now is an ideal time to explore and master this state-of-the-art architecture. Industry experts predict more adoption of Jamstack services as well as products in the future. For your information, Jamstack is a modern web development architecture. Using it you can develop static websites in less time and with minimum effort. The frontend and backend of the website are isolated from each other. The purpose of Jamstack is to ramp up speed, security as well as scalability. For your information it uses APIs, markup as well as JavaScript.
<ul>
<li>Mobile-First Design as well as Development</li>
</ul>
The priority is to craft a mobile-responsive website that can be easily and conveniently be navigated by mobile users. Google gives higher ranking to websites optimized for mobiles. More and more people are using their respective mobiles to make purchases. This approach leverages the built-in features of mobiles to the maximum.
<ul>
<li>Multilingual Websites</li>
</ul>
Websites available in only one language cater to a limited audience. By being multilingual websites have global reach transcending national and regional boundaries. There exist plugins in WordPress to make any site multilingual.
<ul>
<li>Motion UI</li>
</ul>
This is a contemporary design style that transforms websites as well as apps. Motion UI can be used to incorporate transitions and animations in websites. As a result, it captures the attention of viewers and keeps them hooked to the site. You can place motion UI anywhere on a particular website. One significant advantage is that it can be used with all web technologies without issues of compatibility.
<ul>
<li>Blockchain</li>
</ul>
This technology enables smooth digital transactions irrespective of industries. The former provides superior security as well as reliability. Blockchain currently is able to support the majority of websites as well as apps. Today, <a href="https://coffeebeans.io/blogs/building-the-next-gen-supply-chain-with-blockchain-technology" title="Blog">Blockchain</a> as a service is available making Blockchain technology usable as well as economical. As a result, many businesses can leverage this modern and highly useful technology. Blockchain is popular in websites with payment gateway integrations and related to finance. The technology takes care of compliance with existing regulations and ensures that owners have full control over their respective data.
<ul>
<li>Headless CMS (Content Management System)</li>
</ul>
Here the Content Management System (CMS) is separated from the front-end. As a result website development becomes more flexible as well as scalable. The frontend can be quickly changed without affecting the content. Content can be managed as well as updated irrespective of the nature of the frontend.
<ul>
<li>Serverless Architecture</li>
</ul>
Programmers can go about developing websites and not be bothered about the server infrastructure. Benefits of serverless architecture are minimal operational expenses as well as reduced latency. Programmers leveraging serverless architecture do not have the responsibility of managing as well as maintaining servers. This is taken care of by FaaS (Function as a Service) providers whose sole responsibility is serverless computing.
<ul>
<li>Cybersecurity</li>
</ul>
Cyber-attacks are becoming more frequent and sophisticated. Companies are adopting multi-factor authentication to protect their websites. Biometrics are used to identify and authenticate users. These include fingerprint scan, eye scan, voice recognition and more. It is a must to invest in firewalls and quality anti-virus software. Educating the staff of the organization on safe cybersecurity practices is mandatory to be safe.
<ul>
<li>Low-code and No-code Tools</li>
</ul>
Earlier programmers had to master the programming language to develop websites. Today individuals with little or no programming knowledge can develop sophisticated websites rich in functionality as well as features.  There is a rise in the popularity of using low-code and no-code tools for the development of websites. These tools usually have a user-friendly visual interface with drag and drop functionality.
<ul>
<li>WordPress</li>
</ul>
An impressive 43% of websites employ WordPress. The platform is user-friendly and is easy to learn. Headless WordPress will become more popular in the future. Developers can choose multiple front-end technologies in this architecture.
<ul>
<li>Voice Search</li>
</ul>
The inclusion of a voice search facility takes a website to another level. Benefits include hands free browsing and convenience to the differently abled. As per Google, 27% of users do voice search on mobiles. According to GlobeNewsWire more than 50% of US citizens do voice searches at least once daily. In a month over 1 billion voice searches take place.
<ul>
<li>Content Personalization Leveraging AI (Artificial Intelligence)</li>
</ul>
Customer data is processed using ML (Machine Learning) algorithms. This is used by leading organizations such as Netflix, Spotify, YouTube to curate content aligned with the viewer's tastes. A wide range of machine learning algorithms as well as techniques are leveraged by AI to deliver personalized content. Today’s customers appreciate and expect personalized content. The result is enhanced customer engagement and retention. AI also provides recommendations created by analyzing past customer behavior.
<ul>
<li>Progressive Web Apps (PWAs)</li>
</ul>
These match the features as well as performance offered by a native mobile app. At the same time PWAs are as accessible as websites. Advantages include fast loading times and offline access. Even if there is no Internet connectivity, PWAs work. PWAs are built using web technologies including HTML, CSS, and JavaScript. The beauty is that they work on any device. A useful feature of PWAs is push notifications. Finally PWAs boast of superior UX( User Experience) and SEO optimization.
<ul>
<li>Sustainability</li>
</ul>
Clients and customers are increasingly aware of the importance of preserving the environment. Hence sustainability is a rising as well as popular trend in web development in 2024. This can be achieved by reducing software code as well as opting for renewable energy sources in data centers. Website development should be done in a way that is energy efficient as well as reduces carbon footprint. Clients and customers prefer web development entities that prioritize sustainability.
<ul>
<li>Micro Frontends</li>
</ul>
The frontend is divided into multiple components. Each component is developed independently. You have the freedom to use whatever technology you wish in each module. Updates and maintenance become easier. You can make changes to one module which does not affect the other modules. Even if one module goes down the other modules will still be working. So, there is no downtime. Other benefits include superior performance as well as user experience.
<ul>
<li>The use of AI (Artificial Intelligence) in Front-end Development</li>
</ul>
AI tools are used to generate superior code. Other uses include automation of repetitive work and making design processes better. Front-end programmers become free to do complex and creative work. Benefits include superior efficiency as well as productivity. Examples of AI tools include TensorFlow.js, Brain.js and AI Painter. AI testing tools such as Testi.IO are used for automated testing targeting the frontend.
Now that we have seen the top web development trends for 2024 and beyond it is time to take the next step which is to develop your own website.
<h1>How to Go About Developing Your Website</h1>
If you have the in-house capability, you may consider developing the website yourself. If you do not have, consider the option of outsourcing. Freelancers are relatively inexpensive but run the risk of being unreliable. The former may desert you in the middle of the project leaving you high and dry. A better option would be to hire the services of a reputed software outsourcing firm. Usually, they have many employees. So even if a few leave the project they can be replaced by someone equally capable. Do not just hire any outsourcing firm. Go through their website to know more about their capabilities. Ask for references and speak at length with their past clients. Brainstorm with your team to create a list of questions to ask from the past clients. Evaluate the outsourcing firm’s technical as well as domain expertise. Sign a NDA (Non-Disclosure Agreement) to keep your business secrets confidential. Have a SLA (Service Level Agreement) to fix the timeframe of response. Finally choose a partner keeping your unique needs, budget as well as deadlines in mind.
<h1>Why CoffeeBeans?</h1>
<a href="https://coffeebeans.io/" title="website link">CoffeeBeans</a> has the relevant expertise as well as experience in designing and developing robust and quality websites. We have a track record of fully meeting client needs as well as expectations. CoffeeBeans has a team of talented professionals proficient in different technologies as well as domains. We have developed a mature process of website development. Our past clients can vouch for our stellar quality as well as adherence to deadlines. By partnering with us, you get deliverables within the agreed budget as well as with all the promised features and functionality. We believe in transparency and real-time response. Our relationship does not end with the delivery of the website. We offer 24X7 support and maintenance as well. CoffeeBeans will provide all technical documentation to you. We will ensure that your website is free of bugs and performs as expected. Reach out to us to know more about how we can help you meet your goals and objectives. CoffeeBeans takes pride in coming up with innovations in web development as well as devising future-proof web development strategies.

As per Curate Labs as of 2024, there exist roughly 2 billion websites online, that includes 1.13 billion on the World Wide Web. The question in everybody's mind is what to expect in web development in 2024. We look at how web development is evolving in 2024.

The Future of Web Development: Trends to Watch in 2024 and Beyond

<a href="https://bit.ly/4ccknSF">Mobile app development</a> tools help programmers craft apps for different types of mobile devices. The former helps in creating software programs, carrying out debugging, quality assurance, testing and deployment. In 2023, the value of the mobile app market was estimated to be $288.98 billion. Experts estimate that figure will rise to $567.19 billion by 2030. We look at how to shortlist the suitable mobile app development tools. Today there are  mobile  apps available for different uses and purposes. Many of these can be downloaded from the Apple Store or Google Play Store. 
Buying the mobile app development tools should fit in your budget. Another factor is your mobile app's requirement in terms of features, functionality, and business use. Ask yourself what skills your in-house team have. Accordingly buy or use the mobile app development tools. If you do not have expertise hire external human resources or outsource the development work to an organization specializing in mobile app development services. The decision also depends on which platforms you want the mobile app to run on. Whether it is Apple devices, Android devices or other kinds of devices.
One important factor is the level of coding support your existing team needs. If they are proficient in programming and have experience and expertise in programming language you need not bother. If they are limited in terms of programming language then there are apps which need limited programming language. In the worst-case scenario if your team has absolutely no programming language there are mobile app development tools that will do the job even if the user has no programming language.
<img src="https://media.coffeebeans.io/WhatsApp%20Image%202024-08-06%20at%2011.38.09%20AM.jpeg" alt="">
<h1>Different Kinds of Mobile Apps</h1>
<ul>
<li>Native Mobile Apps - They run only on one platform - iOS or Android or Others. Advantage includes high performance and 100% use of native features. Cons are you have to develop a separate app for each platform driving up the cost, effort and time.</li>
<li>Web-based Mobile Apps-These execute on web browsers. Foundation is web technologies mostly HTML,CSS and JavaScript.</li>
<li>Hybrid Apps - Consist of features of native and web apps.</li>
<li>Cross-Platform Mobile Apps - Created once and run on multiple platforms without making any changes to the codebase.</li>
</ul>
We take a look at some of the leading mobile app development tools. If some wonder which of these is a tool for creating mobile apps the answer is all of them are in some way? There are tools for ios mobile app development, multi platform mobile app development as well as cross platform app development. This is a brief list of recommended tools for app developers.
<ul>
<li>Native Script</li>
</ul>
Native Script is an open-source programming framework. It helps build native mobile applications using technologies such as JavaScript, Typescript, Vue.js and Angular. Coders can develop both iOS and Android apps using a single codebase. For your information this programming framework utilizes similar APIs present in Xcode and Android Studio. Their major benefit is the ability to craft applications for multiple platforms in less time and less money. Miscellaneous advantages include robust backend support, code reusability, near native performance, access to Android and iOS APIs.
<ul>
<li>JQuery Mobile</li>
</ul>
It is a cross platform mobile app development framework. JQuery Mobile is popular to craft mobile as well as web applications for various devices. Thanks to it a single code version executes across all devices. It offers useful plugins such as Pop-Up Boxes, Content-Slider, Image Slider, and others. The framework enjoys compatibility with contemporary mobile application development frameworks including PhoneGap. Miscellaneous advantages include quality theming framework, collection of add-on plugins, lightweight nature, and responsive feature.
<ul>
<li>Framework 7</li>
</ul>
It is an open-source programming development framework. Coders use it to craft native desktop, mobile as well as web applications. The platform includes support of Vue.js and React components too. There exists a repository of UI elements as well as widgets including form elements, list views, side panels, action sheets and more. Miscellaneous advantages include convenient maintenance, mature ecosystem as well as convenient customization. For your information, it was released in the year 2014. Framework 7 is extremely easy and simple to learn. Notable features include built-in helper libraries.
<ul>
<li>Appcelerator Titanium</li>
</ul>
For your information Titanium is the creation of Appcelerator Inc. It is an open-source programming framework used to craft mobile apps for each platform. The former leverages a JavaScript-based SDK. There are more than five thousand APIs for Android, iOS, HTML5 as well as Windows. Miscellaneous benefits include shorter development process, enhanced code reusability, convenient API integration as well as API Management functionality.
<ul>
<li>Thunkable</li>
</ul>
This is a no-code platform. Coders use it to craft mobile apps leveraging a visual drag and drop interface. Even individuals lacking knowledge of coding can craft apps for both iOS and Android devices. Available is a rich set of components and functionality such as push notifications, GPS as well as camera. There exists a helpful significantly large and growing community to help coders in their development efforts. The platform is equally adept at crafting apps for both personal and commercial purposes. It has the facility to work offline. It quickly adjusts to the user’s location as well as preferences. It comes with over fifty design components and simple animations.
<ul>
<li>OutSystems</li>
</ul>
This is a low-code platform. It shortens the creation span of even the most sophisticated and complex applications. OutSystems gives the power to coders to craft mobile apps with minimal manual programming. Custom code can be included depending upon requirement. The platform is compatible with different platforms. The company has a free trial period in which you can test its functionality before putting it for live use. This low-code development platform is used to craft mobile as well as web apps that execute in the cloud, on-premises or even in hybrid environments.
<ul>
<li>Apache Cordova</li>
</ul>
It is a stable platform designed to craft hybrid mobile apps. Apache harnesses web tools including HTML, CSS as well as JavaScript. Cross-platform apps crafted with Cordova execute on different OS such as Windows, iOS as well as Android.  A highlight is the platform's rich plugins. Native device functionality such as GPS, accelerometer as well as camera is on offer. The open-source feature attracts it to coders. Finally, the platform has numerous useful tools and resources. For your information Nitobi developed this particular mobile application development framework. It was formerly known as PhoneGap. Programmers do not have to use platform specific APIs such as the ones in Android, iOS and Windows Phone. The output apps are hybrid. To put it simply, they are not 100% native mobile apps or 100% web based. Did you know that Apache Cordova has the ability to be extended with native plug-ins?
<ul>
<li>Xcode</li>
</ul>
This platform is designed for app development targeting Apple Devices. Useful functionality includes a convenient debugging process and real-time insights. There is a rich set of visual components. Xcode offers shortened development span and ease of use. Xcode is Apple 's  popular and powerful integrated development environment (IDE) for macOS. It is utilized to craft software for macOS, iOS, iPADOS, watchOS, tvOS and visionOS. For your information Xcode was first released in 2003. Xcode supports many programming languages such as C++, Python, Ruby and Swift. Another useful functionality is the availability of command-line tools.
<ul>
<li>Ionic</li>
</ul>
This is an open-source platform. Famous for crafting cross-platform mobile apps using core web technologies such as HTML, JavaScript as well as CSS. The apps have a single codebase that executes on multiple platforms. Has an extensive repository of plugins as well as UI components. Known for convenient integration with other existing platforms. For your information Ionic apps execute combining native code and web code. Ionic has a command-line interface (CLI) facility too. It was invented by Max Lynch, Ben Sperry, and Adam Bradley in 2013. It is popular for producing high quality as well as interactive mobile apps. Note that Ionic sometimes uses Cordova.
<ul>
<li>React Native</li>
</ul>
It is a leading platform for crafting cross-platform mobile apps. The famous social networking company namely Facebook developed this popular and powerful platform. In this environment coders employ JavaScript and React programming languages. Programmers just have to write once and distribute it to multiple platforms. Coders can view real-time changes. There is a sizable and supportive development community at hand. Notable for its rich ecosystem and high performing mobile apps. React does not use HTML or CSS. The platform is leveraged to craft the Android and iOS apps at Shopify, Microsoft as well as Facebook. Did you know that React Native is harnessed to build virtual reality apps at Oculus?
<h1>Why CoffeeBeans?</h1>
<a href="https://bit.ly/3LSzsht">CoffeeBeans </a>has the relevant expertise and experience in the domain of mobile app development. We have a pool of talented and qualified programmers skilled in different mobile app development tools. CoffeeBeans is an established organization with competence in outsourcing mobile app development. You do not have to search for developers skilled in different mobile app development tools. The responsibility of obtaining the right human resources is ours. Whether you are looking for native apps, hybrid apps, web apps or cross-platform apps we do the job within your budget and stipulated deadlines. As we are based in India, we are able to provide stellar service at reasonable rates. Note that we are completely transparent in our processes. CoffeeBeans responds in real-time in case of any doubts or queries from your end. Our clients can vouch for our quality of services. Our domain experts will brainstorm with you regarding the desired features and functionality of your desired mobile app. Whether you are looking for inexpensive mobile apps or mobile apps with high performance rest assured CoffeeBeans will do the job. For your information we leverage mature processes to meet your specific goals and objectives. If you desire you can avail of our support and maintenance services even after getting the deliverable. For stellar quality mobile phone app development look no further than CoffeeBeans.

Mobile app development tools help programmers craft apps for different types of mobile devices. The former helps in creating software programs, carrying out debugging, quality assurance, testing and deployment. In 2023, the value of the mobile app market was estimated to be $288.98 billion. Experts estimate that figure will rise to $567.19 billion by 2030

Top 10 Mobile App Development Tools An Overview

By 2025, the market for on-demand apps is projected to reach $335 billion. Today there is instance access to items and services on a  worldwide scale. Examples include Uber for cab services, Uber Eats for food delivery, Amazon for groceries and Netflix for entertainment. Let us look at some more statistics. The revenue in the online food delivery space is estimated to touch $1.22 trillion this year. By 2026, the revenue of the video-on-demand sector is expected to reach $135,673 million. One just cannot imagine a world without on-demand apps. Contemporary customers have become used to the enhanced user experience as well as the ramped up level of service  offered by on-demand apps. The number of on-demand apps is expected to grow by leaps and bounds in the future. Take note that on-demand application is a buzzword nowadays.
<img src="https://media.coffeebeans.io/The%20Complete%20Guide%20to%20On-Demand%20App%20Development%20@1x.jpg" alt="">
<ul>
<li>Definition of On-demand Apps</li>
</ul>
These apps have the facility for users to order or book services online quickly and conveniently. You do not have to travel anywhere and you can make payment online if you wish. The beauty is you can place the order from the comfort of your residence and have goods delivered at your doorstep. 
<h1>Recommended List of Steps to Develop an on-demand app</h1>
<ul>
<li>Outline Customer Requirements</li>
</ul>
Define who your customers are. Find out their challenges as well as requirements.
<ul>
<li>Go through Competitors Offering</li>
</ul>
Check whether similar solutions are already present in the market. Find out whether you can address an unmet need or improve on existing offerings.
<ul>
<li>Identify a niche</li>
</ul>
Shortlist a specific industry. For example, food delivery, transport, groceries, gifts, entertainment, or others.
<ul>
<li>List down Features and Functionality</li>
</ul>
Write down the different features and functionality you want your app to have.
<ul>
<li>Shortlist a development firm</li>
</ul>
Options include developing the app yourself if you have the in-house expertise. A wise option is choosing a competent and experienced outsourcing firm with expertise in designing and developing on-demand apps.
<ul>
<li>User Experience Design</li>
</ul>
Get UX experts to do this step. Check for user friendliness and smooth navigation.
<ul>
<li>Create Front-end and Back-end</li>
</ul>
The Front-end shows how the app will look to users. The Back-end contains the database and server-logic. 
<ul>
<li>Develop a Minimum Viable Product (MVP)</li>
</ul>
Keeping in mind the target audience quickly develop a prototype of the <a href="https://coffeebeans.io/blogs/how-much-does-it-cost-to-develop-an-app-in-2024-in-depth-guide">app.</a>
<ul>
<li>Incorporate User Feedback</li>
</ul>
Present the prototype to users and obtain their feedback. Incorporate the latter into the prototype. Repeat until the product is perfect.
<ul>
<li>Include Third-party Services</li>
</ul>
Payment gateways are necessary for users to seamlessly and securely make payments. There must be provision for live tracking so that the user can see where his/her order has reached.
<ul>
<li>Rigorous Testing</li>
</ul>
Test whether all the included functions work. Also check for ease of use and security levels. The app should be able to handle stress such as a scenario where a high number of users are using the app simultaneously.
<ul>
<li>Provide After-Delivery Support</li>
</ul>
Even after complete development and deployment there should be support offered. There may be performance issues, software bugs and system crashes. In case the number of users increases you should be able to seamlessly scale up the user servicing capability of the on-demand app. 
<h1>Different Kinds of On Demand Apps</h1>
<ul>
<li>Business to Customer (B2C)</li>
</ul>
Here users can order products or services directly from businesses. For example, ordering pizzas to be delivered home from Domino's.
<ul>
<li>Business to Business (B2B)</li>
</ul>
The supplier and receiving entities are both enterprises. For example, automobile companies ordering spare parts.
<ul>
<li>Customer to Customer (C2C)</li>
</ul>
In Amazon, sellers list down their offerings which users of the app order. The transaction is direct between the buyer and seller.
<h1>Important and Essential Features of On-Demand Apps</h1>
For your information there is a global surge in on-demand mobile application development. We take a look at the core features.
<ul>
<li>Registration and Login</li>
</ul>
The registration facility should be quick and user friendly. Anyone should be able to sign up via social media credentials or telephone numbers. Once registered, the member should be able to login conveniently.
<ul>
<li>In-app Communication</li>
</ul>
This is a useful and necessary feature. For example, on a ride hailing app the rider should be able to call or chat with the assigned driver.
<ul>
<li>Payment Gateways</li>
</ul>
Different payment modes including debit/credit cards, Internet banking, digital wallets and others should be available. Also, the payment process should be quick, convenient as well as secure.
<ul>
<li>Facility of Reviews and Ratings</li>
</ul>
Users should be able to give ratings and reviews concerning the service provider. This step will prove useful to other customers.
<ul>
<li>Live Order Tracking</li>
</ul>
Real-time location as well as progress data is provided to the customers. They will know whether their respective order is on schedule or not.
<ul>
<li>Convenient Push Notifications</li>
</ul>
These may concern new discounts, deals or product launches. The notifications may give customers real-time status related to their order or service.
<ul>
<li>Search as well as Filter</li>
</ul>
Users should be able to easily and conveniently search for desired products and/or services.
<h1>Examples of On-Demand Apps</h1>
<ul>
<li>Parking Apps</li>
</ul>
These help users to book parking spaces in advance. They can also get to know the currently available parking spaces.
<ul>
<li>Laundry and dry-cleaning Apps</li>
</ul>
Laundry is picked up from the customer address and delivered after cleaning to the same address.
<ul>
<li>Freelancing Apps</li>
</ul>
Freelancers get opportunities to work with individuals or organizations having freelancing work.
<ul>
<li>Pet Care Apps</li>
</ul>
Pet care services such as pet grooming, pet boarding and dog walking are offered.
<ul>
<li>Grocery Apps</li>
</ul>
Users can order groceries online and receive them at their place of residence. They have the option of payment by cash on delivery too.
<ul>
<li>Home Services Apps</li>
</ul>
Users can communicate with professionals such as electricians, plumbers, pest control providers and others. All services are available on a single platform.
<ul>
<li>Beauty and wellness apps</li>
</ul>
Users can get beauty treatments, massages, and haircuts at home instead of visiting a parlor or salon. Time as well as traveling expenses are saved. Also, these kinds of services offer convenience and a wide range of options.
<ul>
<li>Healthcare Apps</li>
</ul>
Users can order medicines online. They can have virtual consultations with doctors. Lab tests can be conducted at home. No need to travel to the pathology lab.
<ul>
<li>Ride-hailing apps</li>
</ul>
Passengers can avail of drivers for their transportation needs. Some apps offer carpooling also.
<ul>
<li>Food delivery apps</li>
</ul>
Customers can browse through menus of many restaurants and place orders which will be delivered to their respective addresses.
<ul>
<li>Packers and Movers App</li>
</ul>
The service providers will come home and provide the services. Customers just need to pay while the service provider takes care of everything.
<ul>
<li>Gifts App</li>
</ul>
For special occasions you can have flowers, gifts or cakes sent across to friends or family.
<h1>Advantages of On Demand Apps for your Enterprise</h1>
Nothing less than an expert in on-demand mobile app development will suffice. We take a  look at the top advantages below.
<ol>
<li>Additional Revenue Streams</li>
</ol>
You can offer subscription models for your services. The latter may have more features or discounted rates. This will keep customers locked in for a longer period. Through on-demand apps you can gather customer data which can be harnessed for effective marketing campaigns.
<ol>
<li>Enhanced Communication</li>
</ol>
Usually on-demand apps have facilities for in-app chat or calls. Users can raise queries, give specific instructions as well as obtain real-time feedback.
<ol>
<li>Market Advantage</li>
</ol>
By having an on-demand app you can offer faster, better, and more convenient services to your customers compared to your existing competitors. Today's customers expect and appreciate doing business via the facility of on-demand apps. Your business image will be of a modern enterprise leveraging cutting-edge business practices.
<ol>
<li>Increased Customer Trust</li>
</ol>
On-demand apps let customers track their respective orders in real time. The transparency and convenience offered ramps up customer trust in your brand. They need not worry about where their product is or have apprehensions about delayed delivery.
<ol>
<li>Global Reach</li>
</ol>
Now local players can compete with global entities. Customers are not restricted to those who visit your physical store. Your audience is extended to users preferring online platforms as well as those residing in remote locations.
<ol>
<li>Enhanced Performance</li>
</ol>
On-demand apps do away with middlemen. Customers are connected directly with sellers. This step minimizes delays, gets rid of miscommunication as well as promotes efficiency. Time, money, and effort is saved driving up business reputation, revenues as well as profits.
<ol>
<li>Improved Customer Experience</li>
</ol>
On-demand apps have superior user interface as well as user experience. The navigation is intuitive and the payment smooth as well as secure. This takes the shopping experience to a new high attracting new customers and retaining the existing ones.
<h1>Why CoffeeBeans?</h1>
On-demand apps are the face of your business. You cannot afford to take any chances in selecting a partner for developing an on-demand app for your business. <a href="https://coffeebeans.io/" title="website link">CoffeeBeans</a> is an experienced, trustworthy, and quality company in the field of developing stellar mobile apps. As we are based in India, we offer top notch quality at economical rates. CoffeeBeans has a significant pool of technology experts as well as professionals with diverse domain expertise. We leverage a robust and mature outsourcing model to meet as well as exceed customer expectations. Our clients can testify to our transparency, real-time communication, and stellar support as well as maintenance services. Our experts will brainstorm with you to identify all your requirements, the best tech stack and estimate the required budget and timeframe. Rest assured you will get the on-demand app within the budget as well as stipulated time frame. Note that on-demand apps development is here to stay and thrive in the future.

By 2025, the market for on-demand apps is projected to reach $335 billion. Today there is instance access to items and services on a  worldwide scale. Examples include Uber for cab services, Uber Eats for food delivery, Amazon for groceries and Netflix for entertainment.

The Complete Guide to On-Demand App Development

<a href="https://bit.ly/4cUX5Cd">Cloud technology</a> enjoys widespread popularity and global presence today. Most enterprises are leveraging the immense power of the cloud for tangible as well as intangible benefits. Did you know that worldwide cloud investment is projected to go past $1 Trillion by the year 2027? Cloud technology has existed since decades but it's only in recent years that it has become a dominant technology. Enterprises cannot afford to ignore cloud technology if they desire to survive and thrive in the marketplace.
<img src="https://media.coffeebeans.io/The%20Future%20of%20Enterprise%20Cloud%20Technology_.jpg" alt=""> 
Cloud computing can be defined as the delivery of various services via the Internet. The services include software, networking resources, databases, servers as well as data storage. This facility eliminates the need for purchasing and maintaining on-premises hardware and software. Enterprises can avail of cloud services using an Internet connection. The beauty is that you only pay for resources you use. Enterprises can scale up or down cloud services depending upon their individual growth trajectory. You do not need technical expertise to use the cloud efficiently and effectively. Along with savings in terms of time and money, cloud services can be accessed from any geographical location.
The leading cloud providers currently are AWS (Amazon Web Services), Microsoft Azure, Google Cloud, IBM Cloud, Oracle Cloud, Alibaba Cloud, Salesforce, Tencent Cloud, VMware and SAP Cloud.
We take a look at 5 significant enterprise cloud technology trends to watch out for in 2024.
<ol>
<li>The Widespread Use of Artificial Intelligence (AI) and Machine Learning (ML)</li>
</ol>
Industry behemoths including AWS (Amazon Web Services) have significant investments in the above technologies. Foolproof data security is an existing and growing enterprise demand. Thanks to AI and ML, there is better insight into data access as well as usage patterns. The result is smarter and adaptive security measures. Businesses are able to swiftly identify and mitigate probable security threats.
By virtue of these technologies past data is thoroughly examined enabling enterprises to anticipate the amount of resources required in the future. AI and ML deliver future innovation and efficiencies for various enterprises.
<ol>
<li>Increasing Popularity of Hybrid and Multi-Cloud</li>
</ol>
There are 2 main types of cloud – public cloud and private cloud. Public cloud services are shared by multiple enterprises. However there emerges the challenge of data security and privacy in the public cloud. A private cloud is dedicated to a single enterprise. So, the security and privacy of a particular enterprise’s data is ensured. Multi-cloud is the usage of more than one cloud. Enterprises can assign different parts of their operations to different clouds. This resolves the tricky problem of vendor lock in.  In the multi-cloud scenario, the enterprise is not dependent on any single vendor. Also, money is saved as the enterprise can pick and choose different vendors for their cloud needs. 
In the hybrid cloud model, some resources are deployed on-premises whereas some resources are deployed on the cloud. On-premises environment provides exclusive control over data as well as applications and is better from the security and privacy perspective. <a href="https://bit.ly/3LovgWw">Cloud</a> environment yields benefits such as ease of scalability, saving of money, faster time-to-market and the benefit of flexibility. For your information in 2024, the percentage of large organizations adopting multi-cloud deployment is estimated to increase from 76% to 85%. Note that hybrid clouds are suitable for enterprises having legacy systems. By using the hybrid cloud model private data and applications remain private while the remaining data and applications enjoy the advantages associated with public cloud services.
<ol>
<li>Move towards Sustainability</li>
</ol>
Both cloud providers and enterprises using the cloud are emphasizing on the importance of reducing the global carbon footprint. Decision makers in different enterprises are increasingly looking for environment friendly cloud solutions. As such cloud providers are taking steps to minimize or eliminate the environmental impact of using their services.
It is estimated that the carbon emissions originating from the digital sector are 3-4% of the total amount. Carbon emissions are decreased by including eco-friendly practices and the use of environment friendly technologies. Examples of the latter are energy-efficient data centers as well as renewable energy sources such as solar, wind and others.  The goal of green computing is to bring down energy use as well as carbon emissions at all stages such as data centers, end-user operations as well as production.
<ol>
<li>Edge Computing</li>
</ol>
In the traditional cloud model, the servers are located in remote locations. Whereas in the edge computing model, enterprise data is processed close to the source. This brings down latency as well as ramps up performance. Edge computing makes possible real-time data analysis as well as decision making. This notable 2024 trend is driven by the popularity of 5G, algorithms consuming lesser memory as well as smaller sized processors that consume lesser power.
Edge computing refers to a type of networking architecture that reduces the gap between business applications and associated data sources including IoT devices and local edge servers. In Edge Computing, the computation process as well as data storage is done closer to the source sensors as well as devices. Currently the increasing use of IoT (Internet of Things) devices promotes the edge computing paradigm and makes real-time operations a reality.
<ol>
<li>Prevalence of no-code and low-code Cloud Services</li>
</ol>
Earlier one needed knowledge of programming languages and frameworks to develop software solutions. Thanks to no-code and low-code cloud services even people with absolutely no technical knowledge can develop quality apps, services as well as websites. Using these features aids in bringing down the time, money and effort required to develop software solutions. The number of errors is reduced and product development span is considerably reduced. The beauty is that developers do not need any kind of computing infrastructure or knowledge related to programming.
<h1>Miscellaneous Enterprise Cloud Technologies to Consider in 2024</h1>
There are other important trends in contemporary cloud technology too. As most of us are aware, cyberattacks are increasing in frequency and ability to inflict material as well as reputational damage. As such cloud service providers are heavily investing in the security domain such that the cloud is able to withstand even the most sophisticated of cyber-attacks. Thanks to cloud backups, enterprises can conveniently retrieve any lost data and are able to resume normal operations almost immediately. In 2023, the public cloud management and security services market experienced a growth of a significant $42.4 billion. Cloud security solutions feature authentication rules, data encryption as well as appropriate access control policies.
Enterprises prefer using the AI-as-a-service model available in cloud platforms instead of crafting their own Artificial Intelligence infrastructure.Serverless computing frees developers to manage servers and associated infrastructure. Developers can dedicate their valuable time to just develop, test and finally deploy code. It is expected that the serverless computing market will have a Compound Annual Growth Rate (CAGR) of 23.17% from 2023 to 2028. Containerizing is an important cloud computing trend. In the former, applications are placed in self-contained containers. Containerized applications can be executed on any host with runtime container support with no requirement to make modifications to the code of the application. 
<h1>The Coming Future</h1>
Newer trends in cloud technology are likely to emerge as time goes by. Enterprises need to partner with organizations specializing in cloud technology to extract maximum value from these trends. Not all enterprises have the technical expertise to grasp and implement cutting-edge cloud technology trends. It is best to outsource the work to specialists and obtain the benefits in terms of superior performance, faster execution, cost savings, convenience, and other important factors. As a result, enterprises can stay ahead of their competitors and obtain higher revenue and profits. Look for a partner with a deep understanding of enterprise cloud infrastructure as well as enterprise technology trends.
<h1>Why CoffeeBeans?</h1>
For your information <a href="https://bit.ly/3Y4HhYY">CoffeeBeans</a> has both the experience and expertise in managing cloud deployment. CoffeeBeans is a partner with all the world’s 3 leading cloud service providers namely Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP).  We brainstorm with enterprises to better understand their requirements as well as preferences. As such we are able to suggest the best cloud model for them. We have a large pool of talented and qualified cloud professionals who will handhold enterprises through all the stages of cloud management and deployment. CoffeeBeans understands the benefits of transitioning to the cloud and is skilled in obtaining maximum value for your enterprise. Our team is proficient in enterprise cloud technology as well as enterprise cloud computing services. We make it a priority to be up to date with the latest cloud technology trends which helps us serve enterprises better. Outsourcing to use will help enterprises avoid the hassles, time, money, and effort involved in migrating to the cloud or implementing the latest cloud technology trends. You will find that we are committed to meeting your desired deadlines and ability to execute cloud solutions well within the stipulated budget. As an India based company, we offer top notch quality at affordable rates. We help your enterprise achieve quicker time to market giving your organization that vital competitive edge. Note that transparency is our culture and real-time communication an ingrained habit. Reach out to us to better understand how we can be your preferred cloud partner.

Cloud computing can be defined as the delivery of various services via the Internet. The services include software, networking resources, databases, servers as well as data storage. This facility eliminates the need for purchasing and maintaining on-premises hardware and software. Enterprises can avail of cloud services using an Internet connection. The beauty is that you only pay for resources you use.

The Future of Enterprise Cloud Technology: 5 trends to watch out for in 2024

Today, most organizations are availing cloud technology to meet their unique and specific requirements. This saves them significant time, money, and effort. Managed services involve giving the responsibility of business administration and management to an external party. Managed IT services are the most popular of all the types of managed services. Cloud managed service providers handle the cloud computing activities, storage services, network operations as well as application stacks. As per a report from Markers and Markets the worldwide cloud managed services market size will reach a substantial USD 116.2 billion by 2025. For your information, North America held the maximum share of 33% in the global cloud managed services market in 2023. However, Asia Pacific is expected to have the fastest growth in the coming years.
<img src="https://media.coffeebeans.io/Why%20is%20it%20so%20important%20to%20have%20managed%20services%20and%20cloud%20solutions_.jpg" alt="">
<h1>What are Cloud Managed Services?</h1>
Cloud management services involve a separate entity carrying out daily IT management of the cloud infrastructure of a particular organization. The latter may not have the expertise and experience of administering a cloud environment. So, they outsource the work to a third party. The work may include managing cloud security, computing, storage, vendors, and other responsibilities. If your IT department is not equipped to manage cloud-related activities then it makes sense to hire a third party having the necessary skills and knowledge. We explore how a managed cloud service provider can help your organization gain a significant competitive edge. Note that there are a number of benefits of cloud managed services.
<h1>Advantages of hiring cloud MSP (Managed Service Provider)s</h1>
<ul>
<li>Handling Vendors</li>
</ul>
Dealing with vendors may prove to be a cumbersome process. The cloud managed service provider asks relevant technical questions to vendors. Usually, most cloud managed service providers have cordial relations with the vendors. The former will contact the vendors and collaborate with them to fix relevant as well as pressing issues.
<ul>
<li>Minimize Time to Respond</li>
</ul>
The provider remotely observes, tracks, and repairs any cloud issue quickly. As they are both experienced and proficient they respond in real-time and expertly resolve any technical challenges and performance issues. MSPs are usually faster and more efficient than internal IT teams.
<ul>
<li>Implement Disaster Recovery</li>
</ul>
You never know when and where a disaster can happen. The provider makes sure that the business processes of the organization are not affected by disasters. The former ensures that the important company data is safe as well as secure. Cloud managed service providers skillfully incorporate redundancy and resilience into the existing cloud infrastructure.
<ul>
<li>Superior Quality of Services</li>
</ul>
Cloud managed service providers usually have a service level agreement (SLA) in place. It ensures that they respond to issues within predetermined deadlines. The onus is on the provider to ensure optimum performance and hassle-free maintenance. By leveraging their significant experience and expertise, MSPs deliver better, faster and seamless service. 
<ul>
<li>Centralized Approach to Network Services</li>
</ul>
The cloud managed service provider manages all network activities from a centralized location. Benefits include ramped up staff productivity and seamless access to centralized data. This is leveraged by delegating networking specialists to the respective tasks. 
<ul>
<li>Round the Clock Support</li>
</ul>
Your internal IT team may not be available outside office hours or on weekends. But technical issues can crop up anytime. Most cloud managed service providers offer 24/7 support. Technical issues can cause your systems to fail, interrupting business continuity. In such a scenario your staff is demoralized, your customers are angry and your reputation goes for a toss. Prevent such incidents from occurring and causing damage by outsourcing your cloud infrastructure administration to a reputed cloud managed service provider. This step will prevent reputational damage and the incurring of financial losses.
<ul>
<li>Concentrate on Growing Your Business</li>
</ul>
Any organization’s priority is to generate more revenue and profits. This is achieved by offering better products and/or services. So, if your employees are bogged down by cloud management issues it decreases the time, effort, and money you can devote to your core competencies. Outsource the cloud administration to MSPs and be free to focus on your business.
<ul>
<li>Save Substantial Money</li>
</ul>
You need to pay salaries to the members of your internal IT team. If a new technology emerges, you have to train them on it. All these steps entail major expenses. As MSPs deliver the same services as your internal IT team at a fraction of their cost, it makes sense to hire a competent and reliable MSP.
<h1>Difference between CSP (Cloud Service Provider) and MSP (Managed Service Provider)</h1>
Cloud service providers offer useful cloud-based services such as IaaS (Infrastructure as a Service), PaaS (Platform as a service) and SaaS (Software as a Service). You just need an Internet connection to access such services. Cloud MSPs take care of your technology and infrastructure while a CSP gives access to their infrastructure and technology.  MSP services include infrastructure management and IT-related services including data storage, security, maintenance, and technical support.
<h1>What are the Different Components of Cloud Managed Services?</h1>
<ul>
<li>Ensuring Security and Compliance</li>
</ul>
The systems as well as processes of cloud managed service providers adhere to the relevant laws and regulations. They ensure your applications and data are secure and comply fully with existing regulations. 
<ul>
<li>Management of Applications</li>
</ul>
The provider's responsibility is to manage and maintain the enterprise applications operating on the cloud environment. The tasks include installation of software, regular updates and resolving issues so the applications work satisfactorily.
<ul>
<li>Optimizing Performance and Scaling</li>
</ul>
The providers help enterprises to scale resources as per varying demand. The former strive to optimize infrastructure as well as applications such that their level of performance is the maximum possible. There is the facility to seamlessly scale up or down as per demand.
<ul>
<li>Provision for Disaster Recovery</li>
</ul>
The managed cloud service providers devise robust disaster recovery strategies. Backups of data are regularly taken; redundancies are provided and recovery plans are devised to achieve business continuity whenever a disaster takes place. The providers have team members who have specialization in the domain of data recovery. They expedite the process ensuring minimum downtime. 
<ul>
<li>Tracking and Support Services</li>
</ul>
The cloud infrastructure is observed round the clock by the managed service providers. Application issues are proactively detected and swiftly fixed. Support is accessible by all clients irrespective of the time and their geographical location. 
<ul>
<li>Handling of Infrastructure</li>
</ul>
Components of cloud infrastructure such as networking systems, storage devices, server hardware and others are skillfully managed by the provider. The latter takes care of the infrastructure such that it is able to meet the enterprises requirements round the clock.
<h1>How to Choose an MSP Provider?</h1>
It is advised not to hurry to finalize a managed cloud service provider. Shortlist potential providers and contact them to obtain inputs. Do not take an individual decision. Consult your team members and hold discussions with them. Fix your budget and desired SLA (Service Level Agreement). Contact each provider to find out whether they are able to work within your budget and desired deadlines. Next, evaluate their proficiency and reliability. Ask for the contact details of past clients. Speak to the past clients at length to discover pertinent factors such as quality of delivered service, punctuality, adaptability, flexibility, and others. Have a legal contract in place so that commitment is established. Other deciding guidelines are given below.
<ul>
<li>Should be Able to Complement Existing Expertise</li>
</ul>
An important step is to identify your organization's in-house capabilities. You need to have a suitable cloud managed service provider positioned to complement your existing in-house expertise instead of simple replacement. So, your in-house expertise is not wasted and money is saved in the process.
<ul>
<li>Must have an Effective Strategy Tailored to Your Unique Needs</li>
</ul>
Check whether the service provider's cloud managed service strategy is clear and flexible. The strategy should match your budget, deadlines as well as business goals. Compatibility and clear vision is definitely a major factor..
<ul>
<li>Need to Have Deep Understanding of Your Business</li>
</ul>
Look for providers having experience with organizations in your domain. Thus, cost overruns, delays and errors can be avoided or minimized. The provider should be able to deliver customized services as per your unique needs and preferences. Expertise in similar domains helps the provider to offer faster as well as better service.
<h1>The Importance of Cloud Solutions</h1>
Cloud computing is the supply of vital computing resources such as software, hardware, networking etc to enterprises mostly via the Internet. The advantages include access to services 24/7 and from any geographical location. Cloud providers ensure that there is no loss of data and security is robust. Organizations have to pay only for what they use. They have the freedom to scale up or down the resources they want. A benefit is that you don't have to buy software, hardware and network systems. The cloud provider has these resources and makes them available to you at significantly less cost. By using cloud solutions, businesses experience increased speed, agility and are free to innovate as well as focus on their core competencies.
<h1>Why Choose CoffeeBeans?</h1>
<a href="https://coffeebeans.io/">CoffeeBeans</a> has a substantial pool of qualified and experienced individuals in the domain of cloud managed services. Our team will liaison with your organization to fully leverage the benefits of cloud managed services. We have an impeccable track record of delivering products/services within the stipulated deadlines. Our team is familiar with both local and global government regulations. They will help your organization fully adhere to the relevant regulations. Additionally, we have considerable expertise in cybersecurity. As such we can help organizations to implement best cybersecurity practices in their day-to-day operations. This averts downtime as well as ensures business continuity.
Note that we have prowess in a wide variety of domains ranging from aviation to agriculture. So, we have employees well versed in different domains. As we are based in India, we can offer you high-end expertise at reasonable charges. CoffeeBeans has perfected the outsourcing process. Clients get to know what is happening in their respective projects in real-time. Any doubts or queries are answered instantly. Our technical service team is available 24/7 to deal with any outages. Note that we greatly value quality and set high standards for our technical teams. Our employees are adept in the English language and well trained in soft skills. Partner with us to achieve your technological and domain objectives.

Today, most organizations are availing cloud technology to meet their unique and specific requirements. This saves them significant time, money, and effort. Managed services involve giving the responsibility of business administration and management to an external party.

Why is it so Important to Have Managed Services and Cloud Solutions?

RAG (Retrieval Augmented Generation) is an AI Framework that has been on the rise for answering the questions present in external data not trained on by the LLM. RAG models have been very popular in the past year for these tasks. These models can leverage large external knowledge sources like websites, databases or document collections to improve their ability to generate accurate and informative answers.

Advanced RAG for Enhanced and Optimized Results

<h1>Introduction</h1>
The introduction of DevOps practices revolutionized the software development efforts globally. The organizations that embraced DevOps experienced significant gains in the quality of deliverables and were able to reduce the amount of time, effort and money involved in the software development lifecycle. The worldwide DevOps market was estimated to be around $10.9 billion in 2023. By 2032, it is projected to be about six times larger, touching $63.4 billion.
Now companies are turning to AI-powered <a href="https://coffeebeans.io/devops-as-a-service">DevOps</a>. Did you know that the Generative AI in DevOps market is predicted to reach around USD 22,100 million by 2032? The synergy of AI technology and DevOps paradigm, particularly <a href="https://www.goodfirms.co/artificial-intelligence/bangalore">AI in Bengaluru</a> has given early adopters a competitive edge over other organizations. We look at what AI-powered DevOps is and its immense benefits
<h2>What is DevOps?</h2>
In DevOps, the software development team and IT operations team work together from the onset. Agile methodologies are leveraged in the software development lifecycle. Prototypes are developed and released frequently and delivered to the customer for feedback. The feedback is incorporated and another prototype is developed. DevOps is not just a methodology. Rather it involves a change in culture and mindset in the software development and IT operation teams. To put it simply, DevOps is a transformative software methodology prioritizing communication and collaboration between the software development and operation's respective teams. Benefits are increased quality of software, faster release of software products and/or services. Also, the final product more accurately meets the requirements of the customer/client.
<h3>How AI is revolutionizing software development?</h3>
AI (Artificial Intelligence) refers to technology that helps computing devices to have human intelligence as well as problem-solving abilities. The technology can help process and analyze vast quantities of data. AI applications are superior to humans in terms of speed, accuracy, and performance. Applications of AI include 24X7 customer service, supply of detailed information and data-driven insights as well as predictions. Recently, there has been the emergence of high-performance generative AI tools such as ChatGPT. AI is leveraged in different domains for increased speed, accuracy as well as performance. AI has transformed software development in many ways.
Some of the main features are below
<ul>
<li>Personalization of the <a href="https://coffeebeans.io/blogs/how-much-does-it-cost-to-develop-an-app-in-2024-in-depth-guide">software development</a> process</li>
<li>Reduction of time to test and deploy the deliverables</li>
<li>Achievement of better Project Management</li>
<li>Generation of  better quality software code</li>
<li>Automation of repetitive work</li>
</ul>
Time is critical in the domain of software development. By leveraging AI software companies can go ahead of their peers in the intensely competitive market. Note that the usage of artificial intelligence in agile and DevOps practices provide valuable real-time feedback to software development teams.
<h3>What is the Role of AI in DevOps</h3>
AI is transforming the domain of software creation and operations and is known to achieve unmatched speed, innovation as well as productivity. At the forefront is AI-powered DevOps which efficiently integrates AI tools with the DevOps stages to boost collaboration, automate manual work and transform the lifecycle of contemporary software development. By applying the latest AI technology companies can minimize human efforts and release high-quality software far earlier than before. AI for DevOps is a trend that is here to stay. 
<h3>AI Tools for DevOps</h3>
There are quality AI tools available in the market designed to improve DevOps. Leveraging them enhances both the quality and performance aspects.
<ul>
<li>PagerDuty</li>
</ul>
This AI tool helps manage incidents as well as outages. PageDuty helps DevOps teams to automate the whole process of incident response.There exist alerting as well as on-call scheduling features for infrastructure and services.
<ul>
<li>Sysdig</li>
</ul>
This particular tool helps DevOps teams to track microservices as well as debug complex problems. Additionally, team members are provided crystal clear visibility of containerized environments. Sysdig is also useful for anomaly detection.
<ul>
<li>Amazon Copilot</li>
</ul>
This tool enables DevOps teams to do coding instead of configuration of the underlying platform. Also, it offers enhanced visibility of apps. Copilot has useful features such as observability, logging as well monitoring.
<ul>
<li>StackRox</li>
</ul>
The tool seamlessly integrates with ticketing systems, registries as well as pipelines. StackRox can operate with CI/CD (Continuous Integration/Continuous Deployment) systems such as Jenkins. Useful features include detecting misconfigurations and Kubernetes-native security capability.
<ul>
<li>CodeGuru</li>
</ul>
This is a cutting-edge automated code review AI tool. CodeGuru is able to identify incidences of injection vulnerabilities as well as resource leaks. Useful capabilities include catching bugs and security flaws in code early and quite efficiently.The popular AI Tool uses the power of machine learning as well as give relevant recommendations that ramp up quality.
<h3>Practical Uses of AI in DevOps</h3>
Note that integrating AI in DevOps has numerous practical applications. We take a look at some of them.
<ol>
<li>Optimization of Workflows</li>
</ol>
AI raises overall workflow efficiency by skillfully finding obstacles and making processes simpler. As a result, deliverables can be released in the market earlier and organizations achieve significant cost savings.
2. Managing Infrastructure
AI tools thoroughly examine resource usage as well as skillfully distribute existing resources as per need. This results in bringing down expenses and ramping up efficiency.
3. Robust Security
AI finds as well as resolves vulnerabilities in software code and infrastructure. The technology applies high-end algorithms to go through vast amounts of data and locate patterns suggestive of security vulnerabilities. As a result, the risk of damaging data breaches comes down.
4. Automation of Testing
AI has the capability to automate the whole testing process. So significant time and effort is saved. The technology expands test coverage as well as raises accuracy. As a result, the testing stage is enhanced enabling better quality deliverables.
5. Continuous Integration &#x26; Deployment (CI/CD)
AI automates as well as improves CI/CD processes. The former also reduces the need for human intervention. As a result, the software releases become swifter. The deliverable keeps up with the high quality requirements and the pace of release of fresh functionality is ramped up.
6. Superior Code Review
AI-driven tools examine code, spot errors as well as adhere to high coding standards. Software Developers get improved feedback resulting in superior code quality as well as better adherence to best coding practices. Also, the software development cycle takes relatively less time than before.
<h3>Key Benefits of AI-Driven DevOps</h3>
AI-powered DevOps delivers a number of benefits which boost the software development &#x26; deployment domains. We take a look at some of the major benefits.
<ol>
<li>Increased Scalability</li>
</ol>
As an organization grows there are increased workloads. With the help of AI, DevOps teams can scale up their capabilities easily and conveniently. AI helps manage DevOps processes efficiently and effectively. The former has the capability of solving the challenges of more complex systems and a rise in the number and size of software projects.
2. Superior Collaboration
AI technology helps the collaboration efforts become better, smoother, and faster. This is achieved by automation of routine tasks as well as supplying valuable insights. As a result, the team members have more time to innovate and plan strategies.
3. Bring down Expenses
AI-powered DevOps automate manual tasks and better allocate resources. This results in considerable saving of financial resources. AI also helps decrease downtime. This is achieved thanks to smart monitoring as well as predictive maintenance.
4. Superior Quality and Enhanced Reliability
By leveraging AI, DevOps teams are able to achieve significant improvements in terms of reliability as well as quality. Problems are spotted and fixed before they worsen leading to less bugs and enhanced customer satisfaction.
5. Robust Security
Security of software deliverables is paramount particularly in sensitive domains such as finance and healthcare. AI tools can track errors faster and more accurately than human operators. The technology is used to identify and fix security vulnerabilities if any. The result is robust software which is able to withstand advanced cyber-attacks as well as have minimum episodes of downtime.
6. Improved Adherence to Local and Global Regulations
There are several software-related regulations that have to be read and understood. Humans find it overwhelming to process vast quantities of information and understand it clearly. AI detects whether the organization’s DevOps processes fully adhere to the relevant guidelines instantly. Also, it makes the adherence process easy and quick.
<h3>How to Implement AI in DevOps: Key Steps</h3>
<ol>
<li>Examine the Present DevOps Practices</li>
</ol>
This task should be your first step. Go through the different DevOps processes and stages to determine the areas that can be improved by using relevant AI technology. This step facilitates smooth integration of AI with your existing DevOps practices.
2. List Clear Goals
Outline what your integration goals for AI are. Is it to identify security vulnerabilities, adherence to existing regulations, automation of manual work or others? This helps you to narrow down the AI tools which will help achieve your specific goals.
3. Outline Use Cases
Every organization has use cases requiring the application of AI. The use cases may be enhanced decision-making, optimization of resource allocation, spotting of vulnerabilities and others. Knowledge of the use cases help tailor the AI integration effort to ramp up the effectiveness of your organization's DevOps processes.
4. Select the best AI Tools
The AI tools should be evaluated for compatibility with the existing tools in the DevOps processes, how much effort is required to integrate, scaling capabilities and other factors. Also, the tools should be able to smoothly adapt to future evolution and advancement of the DevOps technologies.
5. Integrate AI Tools and Technologies
The next step is to integrate AI technology into the organization’s DevOps stages. This includes the testing, prototype building, software development, release into the production environment and others.
6. Implement Security as well as Compliance
It is very important that the AI tools are compliant with relevant rules and regulations. Audit the AI application's security features comprehensively. Have robust security features included to protect the AI applications from any cyberattacks.
7. Regularly Check Performance
Staff productivity levels, the quality of deliverables and frequency of prototype delivery are some of the measures that determine how well the AI systems are performing. This information will help you know the areas needing your attention as well as where to tweak the AI systems. So, you can take tangible steps to boost the performance of the AI tools and better achieve your desired DevOps objectives.
<h3>What is the future of DevOps and AI?</h3>
AI technology is only improving day by day. With time, technology experts will become more proficient in integrating AI technology with the DevOps processes. The cost, time and effort in AI &#x26; DevOps integration will come down. Also, there will be increasing awareness of the need to merge AI with DevOps among entities worldwide. Note that modern DevOps practices incorporate strong security measures. Robust  security helps drive operational excellence across the whole organization. In the future, the world will see increasing adoption and implementation of AI &#x26; DevOps.
Do not miss out on including this trend in your organization’s software development approach. Implement this step at the earliest and benefit significantly. All software development companies are advised to implement AI driven software development to save time, money and effort. This move will improve ROI, product quality and overall performance.
<h3>Why CoffeeBeans?</h3>
Be aware that the integration of AI with your existing DevOps procedures is no small task. It is risky to assign the task to entities with limited or no experience and expertise. This approach will mostly fail and set your organization back in terms of time and money. It is wise to approach an established company for this important work. CoffeeBeans has a competent and quality team of professionals with considerable experience and expertise in successful AI and DevOps integration. So, you are relieved of the stress of executing this complex task. Note that we are completely transparent in our processes. We respond in real time to any communication from your end. CoffeeBeans has a stellar track record in implementing AI &#x26; DevOps integration within the promised deadlines. 
We can provide references whom you can communicate with regarding our quality, punctuality and support provided. Our team is up-to-date with the latest and trending AI tools. You will be advised and briefed on which AI tools meet your unique and specific requirements. As we are based in India, we are able to offer top notch services at affordable rates. Since we have been in the DevOps domain for a significant period, our processes are mature and optimized. Reach out to us to have a discussion to learn how best we can help you attain your short-term as well as long-term objectives.

The introduction of DevOps practices revolutionized the software development efforts globally. The organizations that embraced DevOps experienced significant gains in the quality of deliverables and were able to reduce the amount of time, effort and money involved in the software development lifecycle. The worldwide DevOps market was estimated to be around $10.9 billion in 2023

Transforming Software Development and Operations with AI-Powered DevOps

<hr>
Nearly everyone has a smartphone nowadays. Mobiles usually come preloaded with some apps. Users can download free or paid apps from the Google and/or Apple play stores. There exists a huge market for different types of mobile apps. You may have a brilliant idea for an app. But you may not know the total cost of an app that fulfills your unique and specific requirements as well as preferences. In this blog we explore the cost of developing mobile apps in 2024.
The main mobile operating systems in the market are Android and iOS. Android accounts for around 70% of the market while iOS has 28% market share. Note that iOS is restricted to Apple smartphones. Android is adopted by several companies such as Nokia, Samsung, Google, and others. As per Business of Apps currently the Apple App Store has a collection of 2.2 million apps whereas the Google Play Store has 3.3 million apps.
<img src="https://media.coffeebeans.io/How%20much%20does%20it%20cost%20to%20develop%20an%20app%20in%202024_.jpg" alt="cover">
<h2>Complexity of the App</h2>
Basic features such as login feature and dashboard are relatively cheap. It will cost you more for additional features such as payment gateway and social media integrations. Cutting-edge technology includes Augmented Reality, Virtual Reality and <a href="https://coffeebeans.io/primary-services/ai">Artificial Intelligence</a> which are relatively expensive and will drive up the overall app development cost.
<img src="https://media.coffeebeans.io/blogs/image%20(6).png" alt="simple and medium app cost">
<img src="https://media.coffeebeans.io/blogs/image%20(7).png" alt="very complex app cost">
<ul>
<li>Type of Platform</li>
</ul>
The first step to develop an app is to decide on a platform. Available options are Android, iOS, Web, and cross-platform apps. If you have a limited budget, it is recommended to choose any one platform. Subsequently if you can afford it, you can aim for multiple platforms. Below is a table providing the rough cost of different types of apps.
<img src="https://media.coffeebeans.io/blogs/image%20(8).png" alt="image">
Native apps work only on one platform either Android or iOS. They make maximum use of the device’s power, hardware, and features. The drawback of native apps is that they cannot be used by users of the alternative platform. Cross-platform apps work on both platforms. The drawback is that they do not have the high level of performance that native apps provide. The advantage is they can be used by all mobile users. Web apps run on web browsers and have the same look and feel provided by native apps. Cross-platform apps are cheaper and faster to develop compared to native apps. 
<ol>
<li>The length of the development process</li>
</ol>
You get an idea of how long the app development will take only when you are executing it. Attrition, inflation, mistakes, budget constraints can alter the estimated length drastically. So, if you are paying hourly to the development team members your cost will rise. An exception is when you have a fixed price contract in place. Below is the approximate cost of the different stages of the app development lifecycle. 
<img src="https://media.coffeebeans.io/blogs/image%20(9).png" alt="image">
2. Location Matters
The cost of developing an app depends on the location of the developers. If you have developers from the US or Western Europe the cost will be on the higher side. On the other hand, if you have India based app developers, the app development cost will be less. The below table gives the developer rates as per geographical location.
<img src="https://media.coffeebeans.io/blogs/image%20(10).png" alt="image">
<img src="https://media.coffeebeans.io/blogs/image%20(11).png" alt="image">
3. Type of Technology Leveraged
Another cost factor is the programming language, frameworks and databases used. The technology stack varies in cost depending upon the performance offered, how new is the technology and how comfortable it is to use. It is advisable to use open-source technologies as far as possible as this step will bring down the app cost. 
4. Type of Personnel Required
If it is a very basic app, the development can be done by an individual. As the size and complexity of the app increases the number of team members and responsibilities change. Roles include project manager, technical architect, quality analyst, UI/UX designer, programmer, tester, and others. You will have to pay salaries if you have the team members as full-time employees. Salary depends on position, qualification, years of experience as well as the level of expertise. Freelancers can do the job but they may leave in between the project leaving you high and dry. If you opt for an in-house team you need to pay for office space, monthly salaries, hardware, software licenses, cloud provider if need be and perks of full-time employees such as paid leave, medical insurance, and others.
<h4>Why CoffeeBeans?</h4>
For your knowledge, we are based in India and have implemented diverse app development using different technology stacks. So, we have a pool of talented and skilled app developers and other personnel doing their job at an economical rate. Our outsourcing model has matured over time. So, constraints such as different time zones are kept minimal. Even though we are based in India, our clientele is global. Our staff communicate fluently in English, the world’s preferred language for business and commerce. Along with tech expertise, we have experts with different domain expertise. Reach out to us to know how best we can meet your expectations in terms of quality, performance, and speed of delivery.

Nearly everyone has a smartphone nowadays. Mobiles usually come preloaded with some apps. Users can download free or paid apps from the Google and/or Apple play stores. There exists a huge market for different types of mobile apps. You may have a brilliant idea for an app. But you may not know the total cost of an app that fulfills your unique and specific requirements as well as preferences. In this blog we explore the cost of developing mobile apps in 2024.

How Much Does it Cost to Develop an App in 2024? In-Depth Guide

Data is the modern currency with increasing relevance as well as impact. As per IBM, over 2.5 quintillion bytes of data are created each day. Forbes reveals a startling fact that only around 0.5% of all data is analyzed. Also, the U.S. Bureau of Labor Statistics estimates that the demand for data analytics jobs will grow by 25% from 2020 to 2030. With staggering amounts of data generated everyday there is an urgent need to store, process and analyze the data to help organizations properly function and grow. 
We look at the top 4 <a href="https://coffeebeans.io/primary-services/big-data" title="Big Data Analytics &#x26; Data engineering">Data Analytics</a> challenges currently faced by organizations.
<img src="https://media.coffeebeans.io/Top%204%20Data%20Analytics%20Challenges%20Faced%20by%20Organizations.jpg" alt="cover">
<ol>
<li>Integrating Different Types of Data</li>
</ol>
Data in a particular organization may be structured, semi-structured or unstructured. The sources may be websites, social media platforms, CRM, ERP, and others. In order to perform data analysis, the data must be integrated. This means challenges in homogenizing data originating from diverse sources and having different formats.
Major obstacles to data integration today are the existence of legacy systems as well as siloed data repositories. The challenge for organizations is to have an effective strategy and leveraging existing technology to overcome these barriers in order to deliver a unified data ecosystem.
As those involved in the data industry know very well, frequently an organization's data originates from multiple sources. Before data analysis, the whole data needs to be consolidated. Fortunately, there are quite reliable tools to gather data from multiple as well as diverse data sources. These tools assemble all the relevant data and produce comprehensive reports almost flawlessly.
<h4>**2. Resistance by Employees **</h4>
Employees of an organization may distrust or fear the implementation of data analytics. Issues include doubts whether data analytics is needed at all. They may prefer old fashioned methods such as manual data analysis and data predictions based on gut feeling. There may also be reluctance by some to learn new and unfamiliar technologies. Some staff may fear being replaced as their work will be done by automated software. Efforts must be taken to familiarize the staff with the latest data analytics techniques and technology. Training workshops should be organized to familiarize staff with contemporary data analytics techniques and to facilitate knowledge transfer. Each employee must be reassured that they will not lose their position or have their salaries cut. Also, efforts must be put in to make all the staff realize that adopting the new technology will make them better at their work. They will become more valuable contributors to the organization’s growth.
<h4>3. Selecting a Suitable Analytics Tool</h4>
Given the enormous amount of data being generated globally on a daily basis old methods of analytics do not work. Humans simply do not have the capacity to manually access vast and complex volumes of data. Old data analytics tools will make organizations lose their competitive edge. Entities adopting the latest data analytics tools will capture market share and be the leaders. 
Do research on the Internet to shortlist the best data analytics tool available in the market. Of course, there may be constraints such as your budget and whether you have the human resources competent enough to use the tool properly as well as effectively. Read reviews to determine the reputation as well as pros and cons of different data analytics tools. Invest in a quality data analytics tool based on factors such as the size of your organization, the amount and complexity of data to handle and the user friendliness of the tool. 
You may have to hire fresh staff to use the tool in the event you do not have the in-house capability. Otherwise outsource the work to a trustworthy, competent, and experienced firm specializing in the field of data analytics.
<h4>**4. Shortage of Data Professionals **</h4>
Data comes in different formats as well as different sources. New regulations are being enacted regarding the governance data. Not only is the volume of data increasing at an unprecedented pace, so is the complexity. New data analytics tools are being introduced in the market. The demand for data scientists, data analysts, data engineers and other types of data professionals is lagging behind the supply. Many organizations are vying for the relatively limited base of qualified data professionals.
You can offer higher salaries to attract talent if you have the capacity to do so. Otherwise, you can partner with educational institutions to train your existing staff. Online training modules are a good option as participants can learn at their own pace.
Other Data Analytics Challenges Faced by Organizations
Note that the above mentioned data analytics challenges are not the only ones contemporary organizations face. The other data analytics challenges include scalability, performance, business intelligence modernization, data quality assurance, low quality data, data security, budget constraints, absence of a strong data culture and developing a robust data warehousing architecture. Today, there is a pressing need to capture actionable insights from the unprocessed data originating from both internal and external entities. Data Analytics deftly solves this systemic problem. But you need to select the right Data Analytics firm with impeccable ethics and immaculate competence.
<h4>Why Outsource Data Analytics?</h4>
Organizations need to concentrate on their competencies to survive as well as thrive in the market. Data Analytics is a complex discipline with a significant learning curve. By the time your staff learns certain data analytics tools the tools themselves may become obsolete. Fortunately, there are organizations specializing in data analytics tools and techniques. They have a large pool of data professionals with diverse specializations and relevant experience. Save money, time and effort as well as stay up-to-date by aligning with such organizations.
<h4>Why Us?</h4>
The CoffeeBeans leadership and staff are well versed with the challenging data analytics domain. By partnering with us, organizations can have confidence in obtaining tangible value with full transparency at an affordable rate. Our team is up-to-date with modern data analytics strategies and technology which enables us to supply diverse organizations with a seamless as well as highly satisfactory experience. Reach out to us to know how we help facilitate a solution to your current and future data analytics challenges.

Data is the modern currency with increasing relevance as well as impact. As per IBM, over 2.5 quintillion bytes of data are created each day. Forbes reveals a startling fact that only around 0.5% of all data is analyzed.Also, the U.S. Bureau of Labor Statistics estimates that the demand for data analytics jobs will grow by 25% from 2020 to 2030.

Top 4 Data Analytics Challenges Faced by Organizations

You may have heard of DevOps which revolutionized the software development industry. MLOps where ML stands for Machine Learning leverages AI to deliver superior performance as well as value. It includes some of the core concepts associated with DevOps. As per industry experts, the worldwide MLOps market will amount to US $75.42 Billion by the year 2033. In this article, we will learn what MLOps is and the trends and predictions regarding this cutting-edge technology.
<h2><img src="https://media.coffeebeans.io/The%20Future%20of%20MLOps.jpg" alt="cover">What is MLOps?</h2>
MLOps is a set of practices with the end goal of deploying as well as maintaining ML models in production in a stable as well as efficient way. Other relevant goals include streamlining, monitoring as well as automating the relevant machine-learning models.
The current market is experiencing rising demand for optimized and flexible MLOps tools. MLOPs leverage AI as well as borrows principles belonging to DevOps. As a result, machine learning workflows are augmented with typical DevOps properties such as continuous integration as well as collaboration.
We take a look at the top emerging trends of the MLOps Platform.
<ul>
<li>Mounting on Edge AI as well as On-Device</li>
</ul>
Computation as well as data storage devices are positioned in relative proximity to the source. To your knowledge, users are inclined towards edge deployment. The reasons include ramped-up data as well as client privacy. Machine learning solutions experience more responsiveness, value addition and stability.
<ul>
<li>Merging with DevOps as well as DataOps</li>
</ul>
This trend enhances collaboration as well as optimizes processes across existing teams. Incorporating DataOps results in good data quality as well as optimized management of data pipelines. Including DevOps facilitates continuous integration as well as delivery pipelines.
<ul>
<li>The Process will become increasingly automated with time and evolvement</li>
</ul>
Automation will reduce dependence on humans and the quantity of errors. Performance and efficiency will be among the areas where users of MLOps will experience growth. Particularly tasks such as deployment of models as well as tracking will likely be automated. Automation will increase the value and benefits of MLOps in myriad ways.
<ul>
<li>Model Interpretability as well as Explainability</li>
</ul>
There is a rising requirement of interpretable as well as transparent machine learning models. The technology emphasizes including tools as well as practices for why model decisions took place. Lastly, there will be the need for necessary regulatory compliance.
<ul>
<li>Rising Emphasis on AI Ethics and Governance</li>
</ul>
As many of us realize, AI technology cannot be left to determine all decisions. Governments and regulatory bodies across the world will enact rules to ensure ethics are not violated, fairness is maintained and there is sufficient transparency in the implementation of MLOps.
<h3> Here are the top predictions for MLOps in 2024</h3>
<ul>
<li>Diverse and a greater number of industries will utilize MLOps</li>
</ul>
With rapidly growing awareness about the relevance as well as usefulness of MLOps many firms spanning different verticals will adopt MLOps in their day-to-day operations. Currently numerous industries including retails, healthcare as well as financial services are implementing MLOps. The latter enables novel drug discovery as well as precise diagnosis and tailored treatment plans. The retail sector gains valuable insights such as superior demand forecasting and adept customization. The Finance industry gets assistance in efficient as well as effective fraud detection. Other benefits include enhanced risk management as well as optimized trading.
<ul>
<li>Universal Availability of this State-of-the-art technology</li>
</ul>
With the passage of time, MLOps will expand to more and more areas and audiences. Company staff such as domain experts, programmers as well as data scientists will actively participate in leveraging as well as implementing this exciting and futuristic strategy. We call this phenomenon the democratization of MLOps.
<ul>
<li>Improvement and Refinement of MLOps</li>
</ul>
This domain will only improve with time and data inputs. Training on newer and more complex data will result in the improvement of MLOps. It will be more up-to-date, precise as well as effective. New versions of MLOps will emerge in the market and it will be a highly sought-after strategy. There will be increasing demand for human resources well versed with MLOps. Experts with knowledge of MLOps will be offered higher salaries and better positions.
<ul>
<li>Maturing and Acceptance of MLOps</li>
</ul>
MLOps has enormous potential to grow to new heights and expand in other areas. The process will only mature and improve with time. The cost of learning and implementing MLOps will decrease resulting in universal adoption of this AI-powered process. Customers and Clients will demand the leveraging of MLOps by companies. Companies utilizing MLOps will have a competitive edge in the market.
<ul>
<li>Rise in Cloud-Native MLOps </li>
</ul>
As a result of the popularity of Cloud-native MLOps, entities will get access to scalable as well as flexible tools. The platforms are designed keeping MLOp's needs and processes in mind. One can deal with model development as well as deployment while not having the responsibility of the management of the underlying infrastructure.
<a href="https://coffeebeans.io/">CoffeeBeans</a> has the requisite expertise as well as relevant experience in implementing MLOps. Over the years we have built up an impressive track record in this important and indispensable domain. Contact us to know firsthand how we can facilitate your organization's seamless transition to MLOps. Make sure you do not lag behind your competitors by implementing top-notch and efficient MLOps solutions in your area of expertise.

You may have heard of DevOps which revolutionized the software development industry. MLOps where ML stands for Machine Learning leverages AI to deliver superior performance as well as value. It includes some of the core concepts associated with DevOps.

The Future of MLOps: Trends and Predictions Shaping the Next Generation of ML Operations

1) In today’s technology landscape, companies are facing increasing pressure to deliver software quickly and efficiently. However, the challenge of managing complex infrastructure can create friction between development and operations teams, leading to inefficiencies and delays in the development process.
Please tell us the challenges faced due to this friction from your experience.
Interesting to start with this part. This rift between the development and operations team is actually what is called the DevOps divide. 
The most obvious one is a delay in the delivery. With a larger DevOps divide, communication and collaboration is sub-optimal. This can be further worsened if both teams have a difference in vision/priorities/strategies leading to further misunderstanding. With so much difference between the two core teams, the delivery will be riddled with bugs, issues, gaps, process leaks, and delays. 
One other challenge I have faced very often as a DevOps manager is a lack of visibility. Outside the team, not many people are even aware of what is happening. A true form of a closed black box. it is so close you have access to it but it is so opaque that you think nothing happens. This also causes issues with people not that familiar with the operations part as they think no work is happening. When one does not understand infrastructure operations that well, we tend to map it to regular applications. but status updates/jira tickets are and cannot be on the same level. And suddenly no one understands what is happening which over time results in, I don’t know what the Ops team is doing. And now you are left to fight the perception that you are working on something. 
However, the one challenge that is a force multiplier is that this DevOps divide results in finger-pointing and blaming when things don’t work as is. The devs team often says the environment is always unstable hence the productivity was impacted. The ops team says that the logs were clear, you should have just gone and fixed it. The Dev team thinks it is not my work as it is the work of a DevOps engineer and this goes on and on. This finger-pointing goes beyond one incident and often keeps the team in the ‘Storming’ phase in the Tuckerman model. When it hits this you are no longer only dealing with unstable environments but also with people who are not too keen to work together. 
2) What according to you are some of the most common sources of friction between development teams and infrastructure teams?
Incident response and RCA’s are probably the most common source of friction IMO. Actually, it is not directly that. Incident response is a symptom of a much deeper problem i.e. a sense of ownership of the stability of the environment. While the ops team works on creating the infra and the dev teams build the application, these pipelines are what keep the system running. One fault on it means a direct impact and the productivity dips. As serious as it sounds, I have often seen more of where the DevOps person is to fix this and less of, hey let me get my hands dirty. A counterpart to it could be the lack of skill but c’mon, at one point in time all of us were proficient in only one stack. We then evolved. But DevOps comes across as a scary world to many of the devs I have worked with who just literally and figuratively freeze when the pipelines fail. They don’t want to go to the deeper darker world of DevOps 😀 
The other side of this coin is that in many teams the Dev teams are not enabled with enough information to be able to fix the issues. Sometimes it is just expertise and sometimes there is a lack of a how-to. 
We need our DevOps to build environments in such a way that we know, acknowledge, and work towards the idea that one day this environment will be maintained by a developer who doesn’t have god/super admin access.
3) Can you share any examples of how friction between these teams has negatively impacted a project or a company?
What DevOps does is usually the one behind the scenes. They build the backbone of the entire application. You wouldn’t see a state-of-the-art UI as an output to show. but it will ensure that the API’s are not hacked. You will not see database entries in millions of records but it will ensure that all resources scale enough at the right time for those millions of records to be inserted. DevOps usually comes into focus when things break. 
There are plenty of examples of how the divide can lead to a bad reputation for an organization. 
Longer downtime, no disaster recovery planned, Security vulnerabilities, Truck factors of 1 or 2, Innovation dies down as we are always firefighting. 
Let’s say that the dev team plans a rollout with some eye-catching code without informing the Ops team. This leads to increased sales which is great for the company but now with the Ops team in the blind, the infra has not been revamped to handle the new scale. HPA and ASG are not just click of buttons. As a result of this, the site crashes leading to bad business values. 
Another classic example is cloud migration. Anytime we speak of this, the development teams say that this is not on us the DevOps team will take care. But in reality, it is a combined effort as the DevOps team needs to know what to migrate. Without this knowledge from the development team, it is possible that some things are left out, and hence post-migration, there is a downtime, bad user experience, or just significant delays in the migration as it is not working. 
Another thing is a hotfix/patch deployment. When a critical bug is reported, the development team rushes to fix it as it causes an impact on the revenue. While it is busy on this, the DevOps team is unaware and is sipping pina coladas on the beach. Suddenly when all is fixed, the DevOps team gets a message to deploy ‘right away’ as it is a patch. This is immense pressure on the DevOps team who until a few seconds ago were unaware. While emergency patches are unavoidable, including the DevOps team from the beginning helps in reducing the pressure and planning much better. 
4) When it comes to reducing friction, what are some strategies that have worked well in your experience?
The one thing that worked wonderfully well was building a cross-functional team. At CoffeeBeans, we invested time into making DevOps a mindset across the org. All devs had to undergo mandatory training to learn about the basics of ci/cd pipelines. This gave them a vast understanding of how things were built and removed the fear of the big black box. We also had DevOps 101 for the non-technical teams such as business analysts, and talent acquisition so that they know what is expected when we say DevOps and everything related. 
The other tiny effort that made a lot of difference is having a slightly different format of updates in Jira cards. We did not talk about Given-When-Then, we spoke about which environment, what is the downtime, and who are the stakeholders to be informed. We retained Acceptance criteria though. 
Another thing that worked and helped immensely when new people were on board was having a standard set of questions in a checklist for every application we built. This ensured that we were maintaining standardization and consistency across projects and most importantly, we were not missing out on anything critical. This list also helped us chalk out very clearly what the project needs and what it does not. a crucial point that helped us with our client communications. 
5) We often hear about the importance of balancing agility and innovation with stability and reliability. Can you share any insights on how to do this effectively in a complex infrastructure environment?
<ol>
<li>The mantra of any application now is CI/CD. Once setup, this is a significant game changer in the way agility can be infused with stability </li>
<li>Define the vision/priorities/goals and ensure both teams understand that. </li>
<li>Draw a virtual line between components in an infrastructure based on criticality. Must-go ones have to be in a stable state all the time. the other segments that can afford a shift/downtime can be used in innovation and playground</li>
<li>Use IaC</li>
<li>different deployment strategies like A/B, the canary that let you reduce the risk of downtime giving you enough space to explore and innovate</li>
<li>Exhaustive monitoring and alerting</li>
</ol>
6) What are some potential risks or pitfalls to avoid when trying to reduce friction between development teams and infrastructure teams?
<ol>
<li>buy-in from the leadership – Make sure your leadership knows the why behind the what of changes in the DevOps strategies. </li>
<li>Assuming everyone knows – don’t assume everyone knows the reason/rationale/approach behind changes or even that it is happening. Communicate</li>
<li>Not addressing tech debts soon enough</li>
<li>Wanting everything yesterday – This is a double-edged sword. With the time to market being the least ever, it is common for everyone to expect all deliverables yesterday. and for the things we don’t understand, we often tend to underestimate the complexity and time needed. We should leave the work to the experts and have due faith in that. </li>
<li>Unclear processes for change management </li>
<li>Undefined SLAs in change management</li>
</ol>
7) Finally, can you offer any advice or best practices for organizations looking to improve collaboration and reduce friction between these teams?
<ol>
<li>Invest in automation. </li>
<li>Give space and time to clear out the tech debts. This goes a long way in ensuring the rigidity and reliability of the system. </li>
<li>Acknowledge the cultural and mindset differences between the development and operations teams. Their work expects different kinds of working styles. Even their format of Jira updates wouldn’t be the same. </li>
<li>don’t downplay it when someone says it takes me that long to build an environment</li>
<li>if the pipelines are red for long and red often, the root cause is something other than a devops people being a truck factor. </li>
</ol>
<h2>Conclusion</h2>
Reducing friction between development teams and complex infrastructure requires a combination of technical solutions and cultural changes. By adopting DevOps practices, automating infrastructure, using containerization, fostering communication and collaboration, and providing adequate training, you can ensure that both teams are working together towards the same goal, which is to deliver high-quality software quickly and efficiently. With the right approach, organizations can reduce friction, improve efficiency, and deliver software faster and more effectively.

1) In today’s technology landscape, companies are facing increasing pressure to deliver software quickly and efficiently. However, the challenge of managing complex infrastructure can create friction between development and operations teams, leading to inefficiencies and delays in the development process.

Reducing Friction between Development Teams and Complex Infrastructure: Interview with Dhivya, Head of Delivery

From sourcing raw materials from every corner of the world and selling end products across the world, Supply chain management plays a vital role! And technology never failed to do its job! Blockchain has already disrupted the finance industry and the supply chain will be the next
<ul>
<li>By Siva Seetharaman, <a href="https://coffeebeans.io/">CoffeeBeans</a> Consulting</li>
</ul>
<img src="https://media.coffeebeans.io/blogs/1_bIG3LRY-CNqs0qSAUXqvFw.jpg" alt="cover">
Supply Chain Management is becoming one of the most critical areas for businesses to thrive. Manufacturing companies source raw materials from every corner of the world and finished goods are also moved across borders. As the quantum of imports/exports increases, there is a risk that SCM might become a bottleneck especially if there is a lack of planning.
Technology has indeed played a crucial role in transforming this age-old sector of movement of goods. Large enterprises are deploying sophisticated Enterprise Resource Planning systems, and Transport Management Systems to manage their day-to-day operations. But still the current processes and the systems do have their own deficiencies. These deficiencies reduce the credibility of the whole SCM infrastructure.
Challenges in current Supply Chain Infrastructure:
<ol>
<li>As goods get transported from one corner to another corner of the world, one shipment is handled by multiple stakeholders until it reaches the customer. Practically, there cannot be one single ERP that is implemented by all the players in the chain.</li>
<li>The lack of one system results in lack of transparency in the flow of Inventory and Information among the participants. The participants operate in silos and parties like banks and end customers are also often kept in the dark.</li>
<li>As the Supply Chains involve a lot of cross-border transactions, the possibility of mixing counterfeit products into the chain is very much possible and current systems are not sophisticated to identify this and trace it back to the source.</li>
<li>There are a lot of manual data entries and paperwork to be done in the current setup. This compounds missing information, and erroneous transactions resulting in reputational and monetary losses in many cases.</li>
</ol>
Blockchain Technology:
Blockchain Technology is now the buzzword in the world of technology. Blockchain has already made a huge impact in the finance industry. Although there are proponents and opponents to Bitcoins and other currency tokens, the idea of blockchain that makes this happen is indeed foolproof and niche. Following the Finance sector, other sectors have started to explore possibilities to find answers to their problems from Blockchain.
What is Blockchain?
Blockchain is a decentralized digital ledger system that has the capability to receive and record transactions from multiple parties involved in the system. In a traditional database, data is stored in the form of tables. But in a blockchain, the data is grouped and stored as blocks which are in turn connected to existing blocks thus forming a blockchain. A block is the storage unit in the blockchain where transaction information is stored. Along with transaction information, other details like an encrypted number from the previous block, block header, etc. are also stored. Building the blocks involves multiple people vouching for the credibility of the transactions that happen between two parties. Only after transactions are validated, a block will get added to the existing blockchain. It is nearly impossible as of now to change or delete data that goes into the blockchain. The unique feature of blockchain when compared to the traditional database systems is that the data that enter the blockchain are tamper-proof and are recorded forever.
How can Blockchain disrupt the Supply Chain Industry?
As mentioned earlier, blocks are the basics of a Blockchain. Out of the many advantages of this system, three points stand out clearly as game-changers when it comes to implementation in Supply Chain Management. They are Improved Traceability, Minimized human intervention, and Enhanced coordination between the parties. Before getting into the advantages of a Blockchain system in Supply Chain, it is important to understand how to build a blockchain system in a supply chain scenario.
A Classic Supply Chain scenario:
<img src="https://media.coffeebeans.io/blogs/0_RMLMhIkn0M8JHU3y%20(1).png" alt="supply chain">
In the above scenario,
<ol>
<li>A customer places an order with a retailer</li>
<li>To fulfill the requirement, the retailer in turn orders to the distributor</li>
<li>The distributor then places the order to a manufacturer to produce the product</li>
<li>Once the manufacturing is done, the manufacturer fulfills the distributor. The distributor fulfills the retailer</li>
<li>As a final step, the retailer fulfills the customer order.</li>
<li>The retailer or distributor or manufacturer might seek the support of a bank for financial aid.</li>
</ol>
As mentioned above, blockchains can improve the credibility of the system by improving the transparency of flow of information and the traceability of inventory between the parties. But why should a system be transparent? Let’s read the cases below to understand why.
Case 1: Banks should make informed decisions before approving a loan or any other transactions. Let’s assume a case where the manufacturer applies for a loan in the bank. In this case, the bank will have to conduct physical audits of the transactions and documentation before approval. This is a long and error-prone process. In a best-case scenario, if the bank can access all the upstream transactions along with agreements in one place, then it will be easy and foolproof for the bank to approve such requests.
Case 2: Let’s assume the distributor imports finished goods from a manufacturer who is in a different country. At the time of receiving at the port, some counterfeit goods are mixed in the supply chain. When the distributor finds this, he will not be able to figure out who mixed counterfeit into the chain. The recall and reverse logistics will then stand out as new problem statements. To address this problem, the distributor should be part of a system where every single transaction with every single participant is recorded in a single system.
A Blockchain Supply Chain system:
<img src="https://media.coffeebeans.io/blogs/0_9exw-HhJUf-7PdjU.png" alt="supply chain system">
For a blockchain system to work as expected, all the parties who are participating in the supply chain should agree to be part of the blockchain. In this case, the assumption is all the parties mentioned in the above diagram are part of the same blockchain. (Reference <a href="https://hbr.org/2020/05/building-a-transparent-supply-chain">https://hbr.org/2020/05/building-a-transparent-supply-chain</a> )
<ul>
<li>The colored bars mentioned in this diagram are the blocks that get created whenever a transaction is done between two participants.</li>
<li>In the first step, the customer orders a product from the retailer and the retailer gives back an order confirmation. These are two separate transactions. So two separate blocks are created. The green block is the customer side and the blue block is the retailer side transaction.</li>
<li>As the flow moves forward, the blocks keep added to the blockchain. So from customer ordering to customer fulfillment, all the blocks are part of this blockchain and anyone participating in this chain will gain the full visibility of the same.</li>
<li>These blocks now form the blockchain. Every transaction is recorded and every participant of the chain can now access the data. This makes the system completely foolproof and also establishes the ultimate transparency.</li>
</ul>
Now that we know how a blockchain system works in a Supply Chain scenario, below are the key aspects of the technology that will transform the sector.
Improved Traceability: Every step of the flow is entrusted and recorded as blocks. This makes it easier for the parties to trace back shipments to the source. This is highly useful in sectors like farming, and fishing where multiple unknown parties are involved at source. Also, in sectors like Pharma, Art, it is possible to trace back and figure out the exact point when and where a counterfeit came into the supply chain.
Minimized human intervention: Airway bills, Invoices, Handling instructions, Bill of Landing, Export License, and Packing list are some of the many documents that are involved in a supply chain. The worrying part is that the maker checker process in documentation is purely manual and it is prone to errors. With Smart Contracts in Blockchain, the complete industry can go paperless and improve the efficiency multifold.
Enhanced coordination between the parties: If all the parties agree to get on a blockchain, then there will be a seamless flow of information to all the participants. This improves the coordination between the parties in the supply chain and the overall visibility of information and inventory flows.
Conclusion:
The implementation of Blockchain in Supply Chain Management has been tested in the real world by a few companies like Walmart Canada, IBM Food Trust. But these are tested on a small scale and the world is yet to see a full-fledged implementation where Blockchain replaces ERP.
Transparency of information flow between parties, Reduction of IT costs, Elimination of manual efforts, and Reduction of losses incurred due to counterfeit products are some of the key advantages of Blockchain technology over traditional technology. There are different ways of implementing a blockchain network. The network can be either public or private. Also, there are various other value adds like smart contracts which reduce the efforts needed for mundane activities in SCM like contract management. Of course, there are course corrections to be done and blockchain is an evolving technology. But it gives a promising ray of hope in the world of Trade, Commerce, and Supply Chain Management.
References:
<ol>
<li><a href="https://hbr.org/2020/05/building-a-transparent-supply-chain">https://hbr.org/2020/05/building-a-transparent-supply-chain</a></li>
<li><a href="https://www.ibm.com/blockchain/supply-chain">https://www.ibm.com/blockchain/supply-chain</a></li>
<li><a href="https://www.mckinsey.com/business-functions/operations/our-insights/blockchain-technology-for-supply-chainsa-must-or-a-maybe">https://www.mckinsey.com/business-functions/operations/our-insights/blockchain-technology-for-supply-chainsa-must-or-a-maybe</a></li>
<li><a href="https://www2.deloitte.com/us/en/pages/operations/articles/blockchain-supply-chain-innovation.html">https://www2.deloitte.com/us/en/pages/operations/articles/blockchain-supply-chain-innovation.html</a></li>
</ol>
Thanks for reading!

From sourcing raw materials from every corner of the world and selling end products across the world, Supply chain management plays a vital role! And technology never failed to do its job! Blockchain has already disrupted the finance industry and the supply chain will be the next

Building the next-gen Supply Chain with Blockchain Technology

<img src="https://media.coffeebeans.io/blogs/1_Rp6x69Ruif29tN7FYDA1Ng.jpg" alt="cover">
So the question is now: What is Prompt Engineering?
Prompt engineering involves deliberately crafting input instructions to steer AI language models, enhancing output precision and control while generating responses.
Here are some Basic and Advance Prompt Engineering Techniques:
<ol>
<li>Zero Shot Prompting</li>
<li>Few Shot Prompting</li>
<li>Chain of Thought Prompting (COT)</li>
<li>Tree of Thought Prompting (TOT)</li>
</ol>
<img src="https://media.coffeebeans.io/blogs/0_TgsfGF2EwppdFnaB.jpg" alt="reference image">
Let’s discuss each Prompting Technique with code examples.
So for that we will use <a href="https://platform.openai.com/overview">OpenAI</a>, <a href="https://python.langchain.com/docs/get_started/introduction.html">Langchain</a> (Open source framework for development of LLM based applications).
Initial setup code :
<pre><code class="language-python">import openai
import os
from langchain.llms import OpenAI
from langchain.agents import load_tools
from langchain.agents import initialize_agent

from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv())

openai.api_key = os.getenv('OPENAI_API_KEY')
def get_completion(prompt, model="gpt-3.5-turbo"):
 messages = [{"role": "user", "content": prompt}]
 response = openai.ChatCompletion.create(
 model=model,
 messages=messages,
 temperature=0, # this is the degree of randomness of the model's output
 )
 return response.choices[0].message["content"]
</code></pre>
We need to Install openai, langchain and dotenv. We can use the pip command for that. And for OPEN_API_KEY create an account on openai, generate the key and then set it in your environment. Command to set up the OPEN_API_KEY in the environment.
<pre><code class="language-python">export OPEN_API_KEY=sk..........
</code></pre>
Let’s begin:
<ol>
<li>Zero Shot Prompting:</li>
</ol>
Zero shot prompting enables a model to make predictions about previously unseen data without the need for any additional training. So LLM models have already trained on very large data sets, that’s why for some simpler queries they can predict the result easily.
Example:
<pre><code class="language-python">prompt = """
Classify the text into neutral, negative or positive. 
Text: I think the vacation is okay.
Sentiment:
"""
response = get_completion(prompt)
print(response)

Output: Neutral
</code></pre>
So in the above example, LLM model simply predicts the sentiment, because the model has that much knowledge.
2. Few Shot Prompting:
Despite impressive zero-shot abilities, large language models struggle with complex tasks in that mode. Few-shot prompting, using prompts with examples, aids in-context learning and enhances model performance.
Example:
<pre><code class="language-python">prompt = """
A "Lion" is a Big, dangerous animal native to Africa. 
An example of a sentence that uses the word Lion is: We were travelling in Africa and we saw these very dangerous Lions. 
To do a "enthusiasm" means to jump up and down really fast. An example of a sentence that uses the word enthusiasm is:
"""
response = get_completion(prompt)
print(response)

Output: The crowd erupted with enthusiasm as their favorite team scored the winning goal.
</code></pre>
Drawback:
Standard few-shot prompting works well for many tasks but is still not a perfect technique, especially when dealing with more complex reasoning tasks.
Example:
<pre><code class="language-javascript">prompt = """
The odd numbers in this group add up to an even number: 4, 8, 9, 15, 12, 2, 1.
A: The answer is False.
The odd numbers in this group add up to an even number: 17, 10, 19, 4, 8, 12, 24.
A: The answer is True.
The odd numbers in this group add up to an even number: 16, 11, 14, 4, 8, 13, 24.
A: The answer is True.
The odd numbers in this group add up to an even number: 15, 32, 5, 13, 82, 7, 2.
A:
"""
response = get_completion(prompt)
print(response)

Output: The answer is False.
</code></pre>
Here we know that the answer is (15+5+13+7) = 40, which is an even number, But our model is not able to generate it. But we can resolve this problem by using the advanced prompt engineering technique COT Prompting.
3. Chain of Thought Prompting:
Chain of Thought (COT) prompting enables complex reasoning capabilities through intermediate reasoning steps. It helps in LLM models to understand the problem in a more detailed way. Let’s take the same example above. But we will provide some additional reasoning intermediate steps.
Example:
<pre><code class="language-python">prompt = """
The odd numbers in this group add up to an even number: 4, 8, 9, 15, 12, 2, 1.
A: Adding all the odd numbers (9, 15, 1) gives 25. The answer is False.
The odd numbers in this group add up to an even number: 17, 10, 19, 4, 8, 12, 24.
A: Adding all the odd numbers (17, 19) gives 36. The answer is True..
The odd numbers in this group add up to an even number: 15, 32, 5, 13, 82, 7, 2. 
A:
"""
response = get_completion(prompt)
print(response)

Output: A: Adding all the odd numbers (15, 5, 13, 7) gives 40. The answer is True.
</code></pre>
No, we got the right answer, because the LLM model tries to solve the question with steps.
Take one more example:
<pre><code class="language-python"># first ask the answer directly:
prompt = """
please solve (10 + 2) * (-4 - 1), give direct answer
"""
response = get_completion(prompt)
print(response)

#we get wrong answer
Output: The direct answer to (10 + 2) * (-4 - 1) is -72. 
# ask the answer in steps:
prompt = """
please solve (10 + 2) * (-4 - 1), please solve in steps
"""
response = get_completion(prompt)
print(response)

Output:

Step 1: Simplify the expression inside the parentheses.
(10 + 2) * (-4 - 1) = 12 * (-4 - 1)

Step 2: Simplify the expression inside the parentheses.
12 * (-4 - 1) = 12 * (-5)

Step 3: Multiply the two numbers.
12 * (-5) = -60

Therefore, (10 + 2) * (-4 - 1) = -60.

#this is the right answer
</code></pre>
Here we just saw COT is a powerful Prompt engineering technique, If we are passing proper intermediate steps.
4. Tree-of-Thought Prompting :
ToT prompting gives superior reasoning abilities to LLM models, This Tree-of-Thought Prompting technique permits Large Language Models to rectify their errors autonomously while progressively accumulating knowledge.
Let’s try to understand this via example:
First try to solve this with zero-shot prompting.
<pre><code class="language-python">prompt = """
Bob is in the living room.
He walks to the kitchen, carrying a cup.
He puts a ball in the cup and carries the cup to the bedroom.
Then he walks to the study room and
He turns the cup upside down, then walks to the garden.
He puts the cup down in the garden, then walks to the garage.
Where is the ball?
"""
response = get_completion(prompt)
print(response)

Output: The ball is in the cup in the garden.
</code></pre>
Right Answer: The ball is in the study room. But, We got the wrong answer, Now we will try to solve this via COT prompting. Here is the
Example:
<pre><code class="language-python">prompt = """
Bob is in the living room.
He walks to the kitchen, carrying a cup.
He puts a ball in the cup and carries the cup to the bedroom.
Then he walks to the study room and
He turns the cup upside down, then walks to the garden.
He puts the cup down in the garden, then walks to the garage.
Where is the ball?
Think carefully and logically, explaining your answer.
"""
response = get_completion(prompt)
print(response)

Output: The ball is in the living room.
</code></pre>
Still, we are not getting the right answer. Let’s try to solve it with TOT Prompting. The principle is to provide the LLM an opportunity to explore multiple branches of reasoning while self-evaluating as it progresses.
<pre><code class="language-python">prompt = """
Imagine three different experts are answering this question.
All experts will write down 1 step of their thinking,
then share it with the group.
Then all experts will go on to the next step, etc.
If any expert realises they're wrong at any point then they leave.
The question is in sigle quotes:
'Bob is in the living room.
He walks to the kitchen, carrying a cup.
He puts a ball in the cup and carries the cup to the bedroom.
Then he walks to the study room and
He turns the cup upside down, then walks to the garden.
He puts the cup down in the garden, then walks to the garage.
Where is the ball?'
"""
response = get_completion(prompt)
print(response)

Output:
Expert 1: "Based on the information given, Bob starts in the living room and walks to the kitchen while carrying a cup."
Expert 2: "Next, Bob puts a ball in the cup and carries the cup to the bedroom."
Expert 3: "Then, Bob walks to the study room and turns the cup upside down."
Expert 1: "After that, Bob walks to the garden and puts the cup down there."
Expert 2: "Finally, Bob walks to the garage."
Expert 3: "Since the ball was put in the cup and the cup was turned upside down in the study room, the ball should still be in the study room. Therefore, the ball is not in the garage."
</code></pre>
Now we got the right answer. Because LLM creates multiple branches of reasoning and in-progress self-evaluation to solve the problem, And finally merge all the branches together to get the response.
Conclusion:
There are a couple of more powerful prompt engineering techniques like ReAct Prompting, Generated Knowledge Prompting, Self-Consistency, and so on, That we can use while creating a good prompt. We can choose these techniques on the basis of our use cases. Sometimes a basic technique can work instead of the advanced one.
Note: As all we know Prompt Engineering is a topic of research so most of the examples are based on research papers. While LLM models are evolving and in this article, we used model got-3.5-turbo and if you use any other model you might get some different results that is totally ok, because this article is based on how we can write more powerful prompts that solve our business problems.

Today we are all in the world of Generative AI, And we all are using some of the powerful GEN AI tools like ChatGPT, DALL-E, and GitHub Copilot. But the most important part while working with these tools is, How efficient you are in Prompt Engineering.

What developers need to know about Prompt Engineering

An embedded database is a database management system that is integrated within an application rather than being a standalone system. This means that the application has direct control over the database, and the database runs within the same process as the application. Embedded databases are often used in applications where data needs to be stored and accessed locally, without the need for a separate database server.<img src="https://media.coffeebeans.io/blogs/1_iLk9R-LEmBjLw4cVYaehtw.jpeg" alt="cover">Embedded database
Embedded databases are typically used when an application needs to store data locally and the data is not shared with other applications or users. They are often used in desktop and mobile applications, as well as in embedded systems such as IoT devices, where network connectivity may be limited or unreliable.
Some common examples of embedded databases include SQLite, H2 Database, Berkeley DB, and Firebird. These databases are often lightweight, fast, and efficient, making them ideal for use in applications with limited resources. They may also include features such as ACID transactions, replication, or full-text search, depending on the specific needs of the application.
When to use one?
Embedded databases are typically used when an application needs to store data locally and the data is not shared with other applications or users. They are often used in desktop and mobile applications, as well as in embedded systems such as IoT devices, where network connectivity may be limited or unreliable.
One popular embedded database that deserves special mention is Watermelon DB. Watermelon DB is an open-source, reactive database that is designed specifically for use in client-side applications. It provides a fast and reliable way to store data locally, with built-in support for offline data synchronization.
Watermelon DB is ideal for use in mobile and web applications that need to work offline. It uses a data synchronization protocol called Differential Synchronization, which allows the database to synchronize with a remote server while minimizing bandwidth usage and reducing conflicts. Watermelon DB is also highly scalable, making it suitable for use in applications with large and complex data models.
Some common use cases for embedded databases include:
● Desktop and mobile applications that need to store data locally.
● IoT devices that need to store sensor data locally.
● Point-of-sale systems that need to store transaction data locally.
● Gaming applications that need to store game data locally.
● Medical devices that need to store patient data locally.
Popular Embedded databases:
● SQLite: A lightweight, open-source database that is widely used in mobile and desktop applications.
● H2 Database: A Java-based database that is designed to be fast and efficient.
● Berkley DB: A high-performance, embeddable database that is used in a wide range of applications.
● Firebird: An open-source relational database that is used in embedded systems and desktop applications.
<img src="https://media.coffeebeans.io/blogs/1_7tGLxM6IBmX2VaJnIb1iwg.png" alt="database">Embedded databases and its feature
How to choose the correct embedded database?
Choosing the correct embedded database for your application depends on a number of factors, such as the size and complexity of the application, the performance requirements, and the desired features. Here are some steps you can follow to choose the right embedded database for your needs:
· Identify the requirements of your application: Determine what kind of data needs to be stored and how it will be accessed, as well as any performance or scalability requirements.
· Consider the size of the database: Depending on the size of your application and the amount of data you need to store, you may need to choose a database that is lightweight and efficient. Look for databases that have a small footprint and use minimal system resources.
· Evaluate the performance: Consider the performance characteristics of the database, such as query times and transaction processing speeds, to ensure that it meets your requirements.
· Look for required features: Determine which features are essential for your application, such as support for ACID transactions, replication, or full-text search. Make sure the database you choose includes these features.
· Check the licensing: Some embedded databases have more restrictive licenses than others. Make sure the license of the database you choose is compatible with your application’s requirements and licensing model.
Embedded databases are an important component in many desktop and mobile applications, as well as in embedded systems such as IoT devices. These databases provide a lightweight and efficient way to store data locally without the need for a separate database server.
At CoffeeBeans, we have experience and expertise in using embedded databases to build reliable and efficient solutions for our clients’ offline data storage and synchronization needs. If you’re looking for a partner to help you implement offline capabilities in your mobile or web application, we can help. Our team of experts can help you design and implement a solution that meets your needs and provides a seamless offline user

An embedded database is a database management system that is integrated within an application rather than being a standalone system. This means that the application has direct control over the database, and the database runs within the same process as the application. Embedded databases are often used in applications where data needs to be stored and accessed locally, without the need for a separate database server.

Embedded Databases Decoded: Choosing the Right Type for Your Application’s Success

<img src="https://media.coffeebeans.io/blogs/1_n6ZZJWEbyVCFfkU5ZjYMAA.jpg" alt="cover">
Activation functions are functions which are used in the Artificial Neural Networks to capture the complexities inside the data. A neural network without an activation function is just a simple regression model. The activation function does the non-linear transformation to the input making it capable to learn and perform more complex tasks.We introduce non-linearity in each layer through activation functions.
<img src="https://media.coffeebeans.io/blogs/0_Sn7tvyAqn0ZLxsMA.png" alt="neural network">
Let us assume there are 3 hidden layers, 1 input and 1 output layer.
W1-Weight matrix between Input layer and first hidden layer
W2-Weight matrix between first hidden layer and second hidden layer
W3-Weight matrix between second hidden layer and third hidden layer
W4-Weight matrix between third hidden layer and output layer
Below mentioned equations represents a feedforward neural network.
<img src="https://media.coffeebeans.io/blogs/0_o236kGYfgpPHW7mx.png" alt="formulas">
If we stack multiple layers, we can see output layer as a function:
<img src="https://media.coffeebeans.io/blogs/0_yMKRr-NxncVRCOM5.png" alt="formula">
What are Ideal qualities of an activation function:
The activation function generally introduce non-linearity in the network to capture the complex relations between input features and output variable/class.
2. Continuously differentiable:
The activation function needs to be differentiable since neural networks are generally trained using gradient descent process or to enable gradient based optimization methods.
3. Zero centered:
Zero centered activations functions makes sure that mean activation value is around 0. This is important because convergence is usually seen faster on normalized data. I have explained many of the commonly used activation below, some are zero centered some are not. Mostly when we have a activation function which is not zero centered we tend to use normalization layers like batch normalization to mitigate this issue.
4. Computational expense should be low:
Activation functions are used in each layer of the network and is computed a lot of times, hence its computation should be easy and not very computationally expensive.
5. Killing gradients:
Activation functions like sigmoid has a saturation problem where the value doesn’t change much for large negative and large positive values.
The derivative of the sigmoid function gets very small there which in turn prevents the updating of the weights in initial layers during backpropagation and hence the network doesn’t learn effectively. This should be avoided to learn patterns in the data and hence the activation function should not ideally suffer from this issue.
Most commonly used activation functions:
In this section we will go over different activation functions.
The sigmoid function is defined as:
The sigmoid function is a type of activation function which has a characteristic “S” shaped curve which has domain of all real numbers and output between 0 and 1. An undesirable property of the sigmoid function is that the activation of the neuron saturates either at 0 or 1 when the input from the neuron is either large positive and large negative. It is also non-zero centered which makes neural network learning difficult. In almost majority of the cases, it is always better to use Tanh activation function instead of sigmoid activation function.
2. Tanh function–
<img src="https://media.coffeebeans.io/blogs/0_Td9y1hYUN0TVKDRd.png" alt="tanh curve">
tanh curve
Tanh has just one advantage over sigmoid function that it is zero-centered and it’s value is binded between -1 and 1.
3. RELU(Rectified Linear Unit)-
<img src="https://media.coffeebeans.io/blogs/0_38oamhtIXD96wEvE.png" alt="RELU plot">
RELU plot
RELU is one of the many non zero-centered activation function and given this disadvantage it is still widely used because of the advantages it has. It is computationally very inexpensive, does not cause saturation and does not cause the vanishing gradient problem. The RELU function doesn’t have a higher limit, hence it has a problem of exploding activations and on the other hand for negative values, it has 0 activation and hence it completely ignores the nodes with negative values. Hence it suffers from “dying relu” problem.
Dying ReLU problem: During the backpropagation process, the weights and biases for some neurons are not updated because its nature where activation is zero for negative values. This might create dead neurons which never get activated.
4. Leaky RELU–
Leaky RELU is a type of activation function based on RELU function with a small slope for negative values instead of zero.
<img src="https://media.coffeebeans.io/blogs/0_NflMZDRuzHzGskcN.png" alt="Leaky RELU function">
Leaky RELU function
Here, alpha is generally set to 0.01. It solves the “dying RELU” problem and also its value is generally small and is not set near to 1 since it will only be a linear function then.
If we use alpha as hyperparameter for each neuron, it becomes a PReLU or parametrized RELU function.
5. ReLU6-
This version of ReLU function is basically a ReLU function restricted on the positive side.
<img src="https://media.coffeebeans.io/blogs/0_wngoFXXl5o8CcX6X.png" alt="ReLU6">
This helps in containing the activation function for large input positive values and hence stops the gradient to go to inf value.
6. Exponential Linear Units (ELUs) Function-
Exponential Linear Unit is also a version of ReLU that modifies the slope of the negative part of the function.
This activation function also avoids dead ReLU problem but it has exploding gradient problem because of no constraint on the activations for large positive values.
7. Softmax activation function-
<img src="https://media.coffeebeans.io/blogs/0_Utufr76XkwKTfUg1.png" alt="function">
It often used in the last activation layer of a neural network to normalize the output of a network to a probability value that in turn is mapped to each class which helps us in deciding the probability of output belonging to each class with respect to given inputs. It is popularly used for multi-class classification problems.
I hope you enjoyed reading this. I have tried to cover many of the activation functions which are commonly used in Neural Networks. If any mistake found, please feel free to mention and the blog will be corrected.

Activation functions are functions which are used in the Artificial Neural Networks to capture the complexities inside the data. A neural network without an activation function is just a simple regression model. The activation function does the non-linear transformation to the input making it capable to learn and perform more complex tasks.We introduce non-linearity in each layer through activation functions.

What are activation functions and why do we need those?

<img src="https://media.coffeebeans.io/blogs/Untitled-3-12-2048x1152.jpg" alt="cover">
Architectural Differences: 
The fundamental difference between ARM and x86 processors lies in their architectural designs. ARM processors have a simplified instruction set, with each instruction completed in one cycle. On the other hand, x86 processors have a more complex instruction set, offering a wide range of instructions, some of which may require multiple cycles to execute. While ARM processors execute a finite number of commands, x86 processors have a much larger instruction set, with approximately 981 instructions in x86-64 architecture. This difference in architecture affects the execution efficiency, programming complexity, and overall performance of the processors.
RISC vs CISC and Development Impact:
The RISC architecture of ARM processors emphasizes efficiency in cycles per instruction, while the CISC architecture of x86 processors focuses on efficiency in instructions per program. RISC processors execute simpler instructions within a cycle at a high clock speed, while CISC processors can perform complex operations with a single instruction. This distinction impacts development as RISC machines may require multiple instructions to execute tasks that can be done with a single CISC instruction. Both architectures have their own advantages and trade-offs, and the choice depends on the specific application and development requirements.
Energy Usage and Efficiency:
ARM processors are designed with a focus on low power consumption and energy efficiency. Their integration into SoCs allows for overall resource management, resulting in lower heat production and simplified cooling systems. This makes ARM processors ideal for mobile devices like smartphones, where battery life is crucial. In contrast, x86 processors prioritize high processing speed and performance, which often results in higher power consumption and heat generation. While x86 processors excel in raw computing power, they tend to consume more energy compared to ARM processors.
Market Trends and Adoption:
ARM processors have gained significant traction in the PC market in recent years. Apple’s transition to ARM-based M-series processors in their Macs has been a game-changer, with Apple accounting for around 90% of ARM-based PCs. ARM processors also power a significant number of inexpensive Chromebooks. Market projections suggest that ARM’s market share in the PC market could grow from the current 14% to 25% by 2027. The availability of more native Arm-based applications and the expertise of existing PC OEMs/ODMs and smartphone manufacturers entering the market contribute to the growing popularity of ARM-based PCs.
<ol>
<li>Advantages and Disadvantages of ARM Processors:</li>
</ol>
<ul>
<li>Advantages:
<ul>
<li>Lower costs: ARM processors are affordable to create and manufacture.</li>
<li>Simple design: ARM processors have a simpler architecture, making them easier to understand and implement.</li>
<li>Low power requirements: ARM processors consume less power, resulting in extended battery life for mobile devices.</li>
<li>Reduced heat generation: ARM processors produce less heat, reducing the need for complex cooling solutions.</li>
<li>System-on-a-Chip (SoC) integration: ARM processors can be integrated into a single chip, resulting in compact designs and efficient resource management.</li>
</ul>
</li>
<li>Disadvantages:
<ul>
<li>Compatibility: ARM processors face challenges with compatibility with x86-based software, such as the Windows operating system. However, this is changing with the availability of ARM-compatible versions of popular software.</li>
<li>Limited calculation capacity: Some ARM processors may have limitations in terms of raw computing power, making them less suitable for resource-intensive tasks.</li>
<li>Dependence on skilled programmers: Due to the simplified instruction set of ARM processors, software optimization and skilled programming are essential to achieve maximum performance.</li>
</ul>
</li>
</ul>
<ol>
<li>Advantages and Disadvantages of x86 Processors:</li>
</ol>
<ul>
<li>Advantages:
<ul>
<li>High computing power: x86 processors are known for their high performance and computing power, making them suitable for resource-intensive applications and tasks.</li>
<li>Wide software compatibility: x86 processors have been in the market for a long time, resulting in a vast library of software and a wide range of compatible applications.</li>
<li>Ease of programming: x86 processors offer a more straightforward programming experience due to their complex instruction set, enabling developers to write efficient code with fewer instructions.</li>
</ul>
</li>
<li>Disadvantages:
<ul>
<li>Higher power consumption: x86 processors consume more power compared to ARM processors, which can result in increased energy costs and shorter battery life in mobile devices.</li>
<li>Heat generation: Due to their high power consumption, x86 processors tend to generate more heat, requiring robust cooling solutions.</li>
<li>Potential cost: x86 processors can be more expensive, especially for high-end models, making them less accessible for budget-conscious consumers.</li>
</ul>
</li>
</ul>
Endnote:
Choosing between ARM and x86 architectures depends on various factors, including the target application, performance requirements, power efficiency needs, and software compatibility. ARM processors excel in power efficiency, compact designs, and mobile applications, while x86 processors offer higher computing power, wider software compatibility, and versatility for PCs and servers. As the market evolves, ARM-based PCs are gaining momentum, driven by advancements in ARM-based hardware, software compatibility improvements, and the convergence of laptop and tablet form factors. It is important to weigh the advantages and disadvantages of each architecture when making decisions about hardware and software compatibility, ensuring that your choice aligns with your specific requirements and priorities.

When it comes to computer processors, two prominent architectures stand out: ARM and x86. While both have their strengths and weaknesses, they are fundamentally different in terms of design and application. ARM processors are based on the RISC (Reduced Instruction Set Computing) architecture, while x86 processors follow the CISC (Complex Instruction Set Computing) architecture. In this blog post, we will delve into the comparison between ARM and x86 architectures, exploring their core differences, impact on development, energy usage, advantages, and disadvantages

ARM vs. x86: Comparing Architectures and Their Impact

Architecture design is a must while developing a SaaS application to ensure its scalability and optimising infrastructure costs. In this blog, Lets discuss the implementation of one such architecture with Quarkus java framework and Hibernate ORM
By <a href="https://www.linkedin.com/in/vishal-bihani-821146148/">Vishal Bihani</a> , <a href="https://coffeebeans.io/">CoffeeBeans Consulting</a>
<img src="https://media.coffeebeans.io/blogs/1_d12p7IPbrPHDTNI_YZeFBg.jpg" alt="cover">
Suppose you have a great product idea, you started working on it and developed a cool SaaS product. You get new <a href="https://en.wikipedia.org/wiki/Multitenancy">tenants</a> to use your product and they liked it. In a nutshell, your product is a hit.
<div class="tenor-gif-embed" data-postid="13865195" data-share-method="host" data-aspect-ratio="1.77515" data-width="100%"><a href="https://tenor.com/view/swimming-in-money-rich-laughing-hysterically-money-dive-jaime-lorente-gif-13865195">Swimming In Money Rich GIF</a>from <a href="https://tenor.com/search/swimming+in+money-gifs">Swimming In Money GIFs</a></div> <script type="text/javascript" async src="https://tenor.com/embed.js"></script>
Everything seems to be going well until you realize you are bleeding money with every increasing tenant. You want to scale up but you are unable to do so because you have already exhausted all your cash.
<div class="tenor-gif-embed" data-postid="16909009" data-share-method="host" data-aspect-ratio="1.33333" data-width="100%"><a href="https://tenor.com/view/broke-money-cash-ugh-bart-gif-16909009">Broke Money GIF</a>from <a href="https://tenor.com/search/broke-gifs">Broke GIFs</a></div> <script type="text/javascript" async src="https://tenor.com/embed.js"></script>
You try to figure out what is going wrong and eventually look into your application infrastructure costs.
You understood that every time a new tenant onboards your application you deploy a new server, database, etc. for that tenant. Separate servers and/or databases are only useful for computing or data-intensive applications but yours is neither of that.
In such scenarios, multi-tenant architecture can be very useful in designing applications with shared resources. Multiple combinations are possible some of which are:
1. Separate servers and shared database
2. Shared server and separate databases
3. Shared server and shared database
<h1>How should I decide which resource to keep separate and which to be shared?</h1>
The decision should be made on each resource after looking at its constraints for scaling up. It’s good if each of your resources scales individually based on the utilization rather than complete application scaling up and remaining underutilized.
There are a few things one should keep in mind while deciding.
For servers:
If your business logic is very customized for each tenant then shared servers are not an optimal solution.
OR
If your application is compute-intensive or each tenant is limited by their respective compute then separate servers are a good solution.
For databases:
Generally, it’s a trade-off between data isolation and resource utilization. Development complexity and scaling constraints are also key decision factors.
A separate database provides tenant-specific customization freedom and ease in monitoring per tenant usage and billing them for backup and disaster recovery.
<h1>Multi-tenant database patterns</h1>
There are three types of patterns:
1. Database per tenant
2. Schema per tenant
3. Row per tenant
Database per tenant
Separated databases will be provisioned for each tenant. It achieves maximum data isolation and will give you freedom for high customization and tenant-specific operations to the database.
The development complexity and cost of this approach are high.
Schema per tenant
Separate schemas are created for each tenant. It achieves data isolation at schema level.
High customization and tenant-specific operations like backup, monitoring, and encryption are not supported in many databases.
The development complexity is high but the cost is low.
Row per tenant
In this approach, all the tenants’ data are stored in the same table. Each row is identified by the tenant identifier. Some databases like PostgreSQL support RLS (Row-Level Security) where the rows are implicitly filtered based on the tenant and there is no need to explicitly write the WHERE clause with the tenant identifier.
There is no data isolation and more care has to be put into writing database queries at the application level.
The development complexity and cost are low.
In this blog, we will discuss the implementation of the shared server and schema-based shared database architecture.
<h1>What we will build?</h1>
We will build an app that will store the information of the users of the tenants.
Each tenant will have its separate schema which will store its users’ details. A public schema that will be accessed by every tenant will contain the pricing detail of the product.
Database pattern
<img src="https://media.coffeebeans.io/blogs/0_VF4dTQkulyUgPx7S.png" alt="data base">
schema-based multi-tenancy database pattern
Tables
<img src="https://media.coffeebeans.io/blogs/0_pxSfBVA6FaifGRS0.png" alt="tables schema">
table details
<h1>Implementation</h1>
One important factor while approaching this solution is whether or not frameworks or tools are available that support this architecture and pattern.
For the implementation part, we will be using the Quarkus framework to deploy the REST APIs and perform the business logic.
The Quarkus is an open-source java framework developed by Red Hat which empowers the developers to build cloud-native and modern java applications. You can learn more about Quarkus <a href="https://quarkus.io/">here</a>.
PostgreSQL will be used as the database. It is an open-source SQL database.
Hibernate-ORM supports schema-based multi-tenancy.
Let’s start
Go to <a href="https://code.quarkus.io/">https://code.quarkus.io/</a> and fill in the application configuration.
<img src="https://media.coffeebeans.io/blogs/0_YRbAMlWwbZm4coUl.png" alt="configure application">
Application configuration
Add the following extensions
<img src="https://media.coffeebeans.io/blogs/0_lHGNJmSSv7_Ct0Gp.png" alt="generate application">
Selected extensions
Open in an IDE and run the following command from the terminal:
<pre><code class="language-shell">mvn compile quarkus:dev
</code></pre>
It will download all the dependencies and start the server with the dev profile activated. In dev, profile Quarkus provides the support for live reloading.
After the server has started, perform a GET request to the following URL:
<a href="http://localhost:8080/hello">http://localhost:8080/hello</a>
It should return ‘Hello RESTEasy’
Create entities
Package information is important, all the entities of the public (shared) schema should reside in the same package. Similarly for the tenant schema. We will discuss this in detail later while discussing multiple persistence units.
<a href="https://medium.com/media/ef34cf9db5e75b5fc6c7b508d068bd1e">https://medium.com/media/ef34cf9db5e75b5fc6c7b508d068bd1e</a>
Hibernate does not support database generation (create or update) in the multi-tenant mode so we need to create the databases ourselves.
Create pricing_details table on public schema
<pre><code class="language-json">CREATE TABLE public.pricing_details
(
 id integer,
 product_name character varying NOT NULL,
 price integer NOT NULL,
 created_at date,
 updated_at date,
 PRIMARY KEY (id)
);
Create schema for Tenant A (GOOGL) and Tenant B (AAPL)

CREATE SCHEMA googl
 AUTHORIZATION postgres;

CREATE SCHEMA aapl
 AUTHORIZATION postgres;
Create user_details table on both schema

CREATE TABLE googl.user_details
(
 id integer,
 first_name character varying NOT NULL,
 last_name character varying NOT NULL,
 created_at date,
 updated_at date,
 PRIMARY KEY (id)
);CREATE TABLE aapl.user_details
(
 id integer,
 first_name character varying NOT NULL,
 last_name character varying NOT NULL,
 created_at date,
 updated_at date,
 PRIMARY KEY (id)
);
</code></pre>
Insert values in the tables.
Create repositories for entities
<a href="https://medium.com/media/b7ad2829e2f9a60984a3d216724202c5">https://medium.com/media/b7ad2829e2f9a60984a3d216724202c5</a>
Configuring application properties
write the following configuration properties in the application.properties
<a href="https://medium.com/media/f8ff107dfcfa6acdebc505b21b946d81">https://medium.com/media/f8ff107dfcfa6acdebc505b21b946d81</a>
Only a single data source is configured because we have only one database to connect to.
A persistence unit defines a set of all entity classes that are managed by EntityManager instances in an application
We have separate persistence units for public schema (&#x3C;default>) and tenant-specific schema (tenant)
The data source is set to &#x3C;default> so that they connect to the default data source. Multi-tenant mode is set to SCHEMA
But we need to configure which persistence unit to manage which entities. For that, we need to create package-info.java files in the packages containing the entity files of public and tenant-specific files.
<a href="https://medium.com/media/2ee973e87d56c5fec30db9633af8fa78Inside">https://medium.com/media/2ee973e87d56c5fec30db9633af8fa78Inside</a> the package containing the entities of the ‘public’ schema
<a href="https://medium.com/media/89a6ee2e71ad4bb1c359c7e059b38b6bInside">https://medium.com/media/89a6ee2e71ad4bb1c359c7e059b38b6bInside</a> the package containing the entities of tenant-specific schema
Okay, we configured which persistence should handle which entities but what about schema information?
Each tenant will have its own schema name then how will the persistence unit know which schema to go for. For this, we will create a Tenant Resolver which will resolve the schema name based on the incoming request.
We need to create a Tenant Resolver for all the persistence units.
We will implement the hibernate’s TenantResolver interface which provides a method to resolve schema names.
<a href="https://medium.com/media/c0c771f1f279bde9dc869a368e7883e9">https://medium.com/media/c0c771f1f279bde9dc869a368e7883e9</a>
The @PersistenceUnitExtension takes the name of the persistence unit for which this TenantResolver implementation is.
@PersistenceUnitExtension is for the default persistence unit.
The @RequestScoped is for defining the lifecycle of this bean same to that of the request the application receives (Bean is created when the application receives the request and is destroyed when the request is completed). Once the request is completed it will be destroyed and will be recreated with every incoming request.
what about the below code?
<pre><code class="language-java">public String resolveTenantId() {
String tenantName = context.request().getHeader("tenant");
 return tenantName;
}
</code></pre>
To keep the implementation simpler we will pass the tenant identifier as the header in the HTTP request with the key as “tenant” and the value as the identifier. The above code will retrieve that value and return it as the schema name for that tenant.
Now all the configuration is done but where are the APIs for fetching data?
<a href="https://medium.com/media/bb3dc31fe4641c24aefcab1dd47d3b1d">https://medium.com/media/bb3dc31fe4641c24aefcab1dd47d3b1d</a>
‘/pricing/all’ endpoint will fetch the list of pricing details from the public schema and return the list.
‘/user/all’ endpoint requires a tenant identifier as a header and will fetch the data from that schema and will return the list of users.
Let’s insert some data into the tables
<img src="https://media.coffeebeans.io/blogs/0_Z-g0_JpRvYd0I3Cg.png" alt="dashboard 1">
<img src="https://media.coffeebeans.io/blogs/0_GKVCl0oNHp8gtUe2.png" alt="dashboard 2">
<img src="https://media.coffeebeans.io/blogs/0_Ve32K5o0OctpcC7c.png" alt="dashboard 3">
aapl schema
Let’s hit the APIs and see the results
We can use postman to check the APIs
<img src="https://media.coffeebeans.io/blogs/0_W-6OJ-AF2p8lMGbD.png" alt="postman pricing api">
list of pricing details from public schema
<img src="https://media.coffeebeans.io/blogs/0_Ym46lKGPjEKP2tD-.png" alt="postman users api">
list of users from google schema
<img src="https://media.coffeebeans.io/blogs/0_FTUlhar0yuykFvYy.png" alt="postman users api">
ist of users from aapl schema
Great! we finally achieved schema-based multi-tenancy for our application.
<h1>Important</h1>
For the simplicity of the implementation, we passed the tenant identifier in the header. It should not be passed in the header. A better approach will be to include it in the JWT tokens which can’t be modified and retrieve that by intercepting the HTTP request and storing the identifier in a TenantContext.
Now I hope you will apply this architecture to your SaaS product and save on your infrastructure costs.
<h1>Source code</h1>
You can find the source code <a href="https://github.com/vishalbihani1306/schema-multi-tenancy">here</a>.
<h1>Pros</h1>
<ol>
<li>Efficient use of resources.</li>
<li>A better architecture for multi-tenancy compared to the row per tenant approach in terms of data isolation.</li>
<li>Low infrastructure costs.</li>
<li>Can easily scale.</li>
</ol>
<h1>Cons</h1>
<ol>
<li>Development complexity is high.</li>
<li>High customization per tenant will be difficult.</li>
<li>Data backup for each tenant is not possible.</li>
<li>Monitoring for each tenant may not be supported.</li>
<li>Frameworks supporting this pattern may not be available in all languages.</li>
</ol>
Thanks for reading.

Architecture design is a must while developing a SaaS application to ensure its scalability and optimising infrastructure costs. In this blog, Lets discuss the implementation of one such architecture with Quarkus java framework and Hibernate ORM  By Vishal Bihani , CoffeeBeans Consulting

Schema-based multi-tenant architecture using Quarkus &amp; Hibernate-ORM

In today’s rapidly evolving threat landscape, organizations need robust security measures to protect their systems and data. Threat modeling is an essential process that helps identify and address potential vulnerabilities early in the software development life cycle. By combining the Security Burrito approach and the STRIDE model, organizations can enhance their threat modeling practices and strengthen their overall security posture. In this blog, we will explore how these two approaches work together to mitigate threats effectively.
<img src="https://media.coffeebeans.io/blogs/Enhancing-Security-1-1-1-2048x1152.jpg" alt="cover">Threat Modeling with the Security Burrito Approach
The Security Burrito approach, emphasizing continuous security throughout the project life cycle, provides a solid foundation for threat modeling. Here’s how it can be applied:
Incorporate Security from the Start: By integrating security considerations from the initial stages of design and development, organizations ensure that security is a fundamental aspect of the project. This includes identifying potential threats and considering countermeasures early on.
Continuous Security Assessment: Adopting a continuous security mindset allows for ongoing assessment of potential threats. Regular security reviews and risk assessments help identify and address vulnerabilities in real-time, reducing the likelihood of security breaches.
Collaboration and Communication: Encouraging collaboration between developers, security professionals, and other stakeholders fosters a proactive security culture. Effective communication ensures that threat modeling activities align with project goals and that potential threats are properly understood and addressed.
Applying the STRIDE Model in Threat Modeling
The STRIDE model is a valuable framework for identifying potential threats in software systems. It stands for the following threat categories:
Spoofing Identity: This includes threats such as impersonation or unauthorized access. Countermeasures may involve implementing strong authentication mechanisms, multi-factor authentication, and robust user identity management.
Tampering with Data: Threats in this category involve unauthorized modification or manipulation of data. Countermeasures can include data validation, input sanitization, and encryption to protect data integrity.
Repudiation: This category focuses on threats related to denying or disputing actions or events. Implementing audit logs, digital signatures, and secure timestamps helps establish non-repudiation and traceability.
Information Disclosure: Threats in this category pertain to unauthorized access or exposure of sensitive information. Countermeasures may involve data encryption, access controls, and secure transmission protocols.
Denial of Service: These threats aim to disrupt or disable system functionality. Countermeasures may include implementing rate limiting, traffic monitoring, and employing mitigation strategies against DoS attacks.
Elevation of Privilege: This category deals with unauthorized access to elevated privileges. Countermeasures may involve implementing strong access controls, privilege separation, and least privilege principles.
Combining the Security Burrito approach with the STRIDE model enhances threat modeling practices:
Continuous threat identification and mitigation: By continuously assessing threats and vulnerabilities, organizations can promptly identify and address security issues using the appropriate STRIDE categories.
Proactive security measures: By integrating security from the early stages and fostering a security-first mindset, organizations can proactively implement countermeasures to mitigate identified threats effectively.
Collaboration and knowledge sharing: The Security Burrito approach promotes collaboration between stakeholders, allowing for collective understanding and action against identified threats based on the STRIDE model.
Conclusion
Threat modeling is a critical component of effective security practices. By combining the Security Burrito approach with the STRIDE model, organizations can strengthen their threat modeling efforts and enhance their overall security posture. This integrated approach ensures that potential threats are identified and mitigated throughout the project life cycle, enabling organizations to build more secure and resilient software systems.
Credits: Lok Bahadur, Software Engineer.

In today’s rapidly evolving threat landscape, organizations need robust security measures to protect their systems and data. Threat modeling is an essential process that helps identify and address potential vulnerabilities early in the software development life cycle. By combining the Security Burrito approach and the STRIDE model, organizations can enhance their threat modeling practices and strengthen their overall security posture. In this blog, we will explore how these two approaches work together to mitigate threats effectively.

Enhancing Security with Threat Modeling Using the Security Burrito Approach and STRIDE

In today’s fast-paced digital world, providing efficient search functionality is crucial for delivering a seamless user experience. Algolia, a powerful search and discovery API, offers developers a way to implement robust search capabilities into their applications. In this blog post, we will explore how to integrate and synchronize Algolia with Strapi (v4) to enhance search functionality for a “Product” collection
<img src="https://media.coffeebeans.io/blogs/BLog-thumbnail-algolia.jpg" alt="cover">
Setting Up Algolia and Strapi Integration:
To begin, we need to set up Algolia and Strapi integration. We assume you already have a Strapi project and have installed Algolia. Now, let’s create a helper function that will initialize the Algolia index.
<pre><code class="language-javascript">const algoliasearch = require("algoliasearch");

const client = algoliasearch(
 process.env.ALGOLIA_APP_ID,
 process.env.ALGOLIA_API_KEY
);

const getAlgoliaIndex = (name) => {
 const index = client.initIndex(`${name}(${process.env.NODE_ENV})`);
 return index;
};

module.exports = getAlgoliaIndex;
</code></pre>
This code sets up the Algolia client using your Algolia app ID and API key. The getAlgoliaIndex function initializes the index with the provided name and environment variables. Note that we have also used the current environment with the index name to differ between the indices of different environments.
Synchronizing Strapi Product Collection with Algolia:
Now, let’s proceed to add and synchronize the “Product” collection in Strapi with Algolia. We will utilize Strapi’s lifecycle hooks to achieve this. Specifically, we will use the afterCreate lifecycle method.
Navigate to the product content type in your Strapi project and locate the lifecycles.js file. If it doesn’t exist, create one in the same directory. Add the following code to the lifecycles.js file:
<pre><code class="language-javascript">const getAlgoliaIndex = require("./path/to/getAlgoliaIndex");

const index = getAlgoliaIndex("product");

module.exports = {
 async afterCreate(entry, data) {
 const { id } = data.result;
 // Query product added to database
 const product = await strapi.db.query('api::product.product').findOne({
 where: {
 id
 }
 })

 try {
 // Save the object to Algolia
 await index.saveObject({ ObjectID: id, ...product });
 console.log("Object saved to Algolia");
 } catch (error) {
 console.error("Error saving object to Algolia", error);
 }
 },
};
</code></pre>
In this code snippet we import the getAlgoliaIndex function we created and then initialize the Algolia index by calling the function and passing a name for the index.
In the afterCreate lifecycle method we query the data which is added to the database.
Finally we use index.saveObject method to save the new entry to Algolia. We wrap this operation in a try-catch block to handle any potential errors.
Conclusion:
By integrating and synchronizing Algolia with Strapi, we have empowered our application with advanced search capabilities. Leveraging Algolia’s powerful search API, we can deliver fast and relevant search results to our users. This integration enables real-time updates to the Algolia index whenever a new product is created in Strapi. Implementing Algolia within Strapi not only enhances search functionality but also provides a scalable solution for managing search operations in your application.

In today’s fast-paced digital world, providing efficient search functionality is crucial for delivering a seamless user experience. Algolia, a powerful search and discovery API, offers developers a way to implement robust search capabilities into their applications. In this blog post, we will explore how to integrate and synchronize Algolia with Strapi (v4) to enhance search functionality for a “Product” collection

Integrating and Synchronizing Algolia with Strapi (v4)

Serverless computing has been gaining popularity as a powerful and efficient solution for building and deploying applications. With serverless computing, developers can focus on writing code without worrying about managing servers or infrastructure. In this blog post, we’ll take a deep dive into the architecture and capabilities of serverless computing, exploring how it works, its benefits and drawbacks, and its potential applications.<img src="https://media.coffeebeans.io/blogs/Blog%20cb.jpeg" alt="cover">
<h1>What is Serverless Computing?</h1>
Serverless computing is a cloud computing model where the cloud provider manages the infrastructure and automatically provisions resources as needed to execute code. This means that developers don’t have to worry about managing servers, scaling, or infrastructure maintenance. Instead, they can focus on writing code and building applications. Serverless computing is often used for building event-driven applications or microservices, where functions are triggered by events and execute specific tasks.
<h1>How Serverless Computing Works</h1>
In serverless computing, applications are broken down into small, independent functions that are triggered by specific events. These functions are stateless, meaning they don’t retain information between executions. When an event occurs, the cloud provider automatically provisions the necessary resources and executes the function. Once the function is complete, the resources are de-provisioned, making serverless computing highly scalable and cost-efficient.
<h1>Serverless Computing Architecture</h1>
The architecture of serverless computing typically involves four components: the client, the API Gateway, the compute service, and the data store. The client sends requests to the API Gateway, which acts as a front-end to the compute service. The compute service executes the functions in response to events and may interact with the data store to retrieve or store data. The API Gateway then returns the results to the client.
<h1>Benefits of Serverless Computing</h1>
Serverless computing offers several benefits over traditional server-based computing, including:
Reduced costs: Serverless computing allows organizations to pay only for the resources they use, rather than paying for dedicated servers or infrastructure.
Improved scalability: Serverless computing can automatically scale up or down depending on demand, making it highly scalable and efficient.
Reduced maintenance: Since the cloud provider manages the infrastructure, organizations don’t need to worry about maintaining servers or infrastructure.
Faster time to market: Serverless computing allows developers to focus on writing code and building applications, reducing the time to market new products and services.
<h1>Drawbacks of Serverless Computing</h1>
While serverless computing has several benefits, it also has some drawbacks, including:
Limited control: Since the cloud provider manages the infrastructure, developers have limited control over the environment and resources.
Cold start times: When a function is executed for the first time, it may take longer to start up, leading to slower response times.
Vendor lock-in: Organizations may be tied to a specific cloud provider, making it difficult to switch providers or migrate to a different environment.
<h1>Some facts about serverless computing</h1>
Serverless computing is often referred to as Functions-as-a-Service (FaaS) because it allows developers to write and deploy individual functions rather than entire applications.
Serverless computing is often used in microservices architectures, where applications are broken down into smaller, independent components that can be developed, deployed, and scaled independently.
Serverless computing can result in significant cost savings for organizations because they only pay for the resources they use. This can be especially beneficial for applications with unpredictable traffic patterns or occasional bursts of computing power.
One of the biggest drawbacks of serverless computing is the “cold start” problem, where a function may take several seconds to start up if it hasn’t been used recently. However, this problem can be mitigated through various optimization techniques.
Serverless computing is often used in event-driven architectures, where functions are triggered by specific events such as user interactions, changes to a database, or changes to a file system. This can make it easier to build highly scalable and efficient applications.
Now, let’s explore some other serverless computing frameworks that can be used in addition to Google Cloud Functions.
AWS Lambda: AWS Lambda is a serverless compute service from Amazon Web Services (AWS). It allows developers to run code in response to events without worrying about managing servers or infrastructure.
Microsoft Azure Functions: Microsoft Azure Functions is a serverless compute service from Microsoft Azure. It allows developers to run code in response to events and supports a wide range of programming languages.
IBM Cloud Functions: IBM Cloud Functions is a serverless compute service from IBM Cloud. It allows developers to run code in response to events and supports a wide range of programming languages.
OpenFaaS: OpenFaaS is an open-source serverless framework that allows developers to run functions on any cloud or on-premises infrastructure.
Apache OpenWhisk: Apache OpenWhisk is an open-source serverless platform that allows developers to run functions in response to events. It supports a wide range of programming languages and can be deployed on any cloud or on-premises infrastructure.
Kubeless: Kubeless is a Kubernetes-native serverless framework that allows developers to run functions on Kubernetes clusters. It supports a wide range of programming languages and can be deployed on any Kubernetes cluster.
IronFunctions: IronFunctions is an open-source serverless platform that allows developers to run functions on any cloud or on-premises infrastructure. It supports a wide range of programming languages and can be deployed on any container orchestrator.
These serverless computing frameworks offer developers a range of options for building and deploying serverless applications. Each framework has its own strengths and weaknesses, so developers should choose the one that best fits their needs.
<h1>Real-time examples</h1>
Coca-Cola: Coca-Cola uses serverless computing to power its Freestyle soda machines, which allow customers to mix and match different soda flavors. The machines use AWS Lambda functions to process customer requests and make recommendations based on their preferences.
iRobot: iRobot uses serverless computing to power its Roomba robot vacuums, which use computer vision and machine learning to navigate homes and clean floors. The Roomba vacuums use AWS Lambda functions to process data from their sensors and decide where to go next.
Capital One: Capital One uses serverless computing to power its mobile banking app, which allows customers to manage their accounts, transfer money, and pay bills. The app uses AWS Lambda functions to process requests and deliver real-time information to users.
Fender: Fender uses serverless computing to power its Fender Play platform, which provides online guitar lessons to users around the world. The platform uses AWS Lambda functions to process user data and generate personalized lesson plans.
Netflix: Netflix uses serverless computing to power its video encoding and transcoding workflows, which are used to prepare video content for streaming on various devices. The workflows use AWS Lambda functions to process video files and convert them into the appropriate format for each device.
Conclusion
Serverless computing is a powerful and efficient solution for building and deploying applications. It offers several benefits, including reduced costs, improved scalability, reduced maintenance, and faster time to market. However, it also has some drawbacks, including limited control, cold start times, and vendor lock-in. Despite these drawbacks, serverless computing will likely become an increasingly popular solution for building event-driven applications and microservices.

Serverless computing has been gaining popularity as a powerful and efficient solution for building and deploying applications. With serverless computing, developers can focus on writing code without worrying about managing servers or infrastructure. In this blog post, we’ll take a deep dive into the architecture and capabilities of serverless computing, exploring how it works, its benefits and drawbacks, and its potential applications.

When to use serverless computing

DeFi, short for Decentralized Finance, is a movement that aims to offer financial services and products that are open to everyone, without the need for intermediaries. DeFi has taken the world of cryptocurrency by storm and is quickly becoming one of the hottest trends in the industry.
<img src="https://media.coffeebeans.io/blogs/Defi.png" alt="cover">One of the key features of DeFi is that it operates on a decentralized platform, meaning that it operates on a network of computers rather than relying on a central authority such as a bank. This makes DeFi transactions faster, cheaper, and more secure compared to traditional financial transactions.
Financial services performed on public blockchains, particularly Ethereum, are referred to as Defi (or “decentralized finance”). With DeFi, you can earn interest, borrow, lend, buy insurance, trade derivatives, swap assets, and do other things, but it’s faster and doesn’t require paperwork or a third party. DeFi, like crypto in general, is worldwide, peer-to-peer (meaning it is sent directly between two people rather than being routed through a centralized system), pseudonymous, and open to everybody.
How Blockchain Plays a Critical Role in DeFi
Blockchain technology is a critical component of DeFi as it enables trust less transactions, eliminates intermediaries, and ensures data immutability. With the use of smart contracts, DeFi eliminates the need for intermediaries and ensures that transactions are executed automatically, transparently, and securely.
Cryptocurrencies leverage blockchain technology, which is employed in decentralized finance. A blockchain is a decentralized and secure database or ledger. The blockchain is managed and transactions are processed via applications known as dApps.
The blockchain stores transactions in the form of blocks, which are later confirmed by other users. If all of these verifiers agree on a transaction, the block is closed and encrypted, and a new block is created with the previous block’s details within.
The name “blockchain” refers to how the data in each succeeding block “chains” the blocks together.
One popular DeFi application is Defi Swap, which allows users to exchange different cryptocurrencies without having to go through a centralized exchange. Defi Swap operates on smart contracts, which are self-executing contracts with the terms of the agreement between buyer and seller being directly written into lines of code. This makes Defi Swap transactions completely transparent and secure, as the terms cannot be altered once the contract has been executed.
Another exciting aspect of DeFi is the use of DeFi Coins. DEFC, for instance, is a DeFi Coin that provides access to a wide range of DeFi services, including Defi Swap. DEFC is unique in that it operates on a dual-chain platform, which means that it operates on both Ethereum and Binance Smart Chain. This allows DEFC to offer fast, cheap, and secure transactions, making it an attractive option for DeFi users.
Defi Advantages and Risks
Defi Advantages
· No Boundaries– DeFi has no territorial restrictions, as is the case with everything in the realm of cryptocurrencies. Theoretically, anyone with access to the Internet and cryptocurrency wallets could communicate with DeFi.
· Greater Speed– A lack of middlemen and borders, along with blockchain technology’s greater speed, leads to quicker transactions and generally simpler access to the services.
· Greater Transparency– A key benefit of various blockchain technologies is the total openness that exists between all stakeholders.
· Programmability– Smart contracts’ flexibility can better-automated automate solutions, which in turn can be utilised to produce new assets and financial instruments.
· Interoperability– The system’s flexibility also makes it simpler to create centralized and decentralised applications (dApps) that complement one another at a deeper level than is conceivable in the realm of conventional financial solutions and services.
· Immutability– The immutability of a blockchain-based decentralised system based on cryptography and data protection promotes security and trust.
· More Command– A single user has more control over their finances thanks to DeFi’s permissionless, transparent, and clear decentralised architecture. To keep track of their DeFi statistics and portfolio, there are numerous alternatives.
Defi Risks:
· Regulatory Danger– DeFi protocols are now operating with absolutely no government control or regulation from any government organisation. Simply said, this position is subject to change, and it is impossible to forecast how new government legislation governing DeFi protocols may influence your DeFi investments.
· Token Danger- Each DeFi investment includes cryptocurrency tokens. For example, if you deposit cash in a Uniswap liquidity pool, your investment exposes you to the pool’s two tokens: the liquidity provider token that you receive after depositing and the Uniswap token that you earn as a reward. If you invest in a stablecoin pool, the pool will most likely contain a mix of different stablecoins.
· Consumer Protection is Lacking- DeFi has thrived in the absence of laws and regulations. However, when things go wrong, users usually have little or no protection. There are no state-run DeFi compensation mechanisms, and no restrictions requiring DeFi service providers to maintain capital reserves.
Here Are Some of The Most Impactful Defi Uses:
1. Decentralized exchanges (DEX): A decentralized exchange is a platform that enables peer-to-peer trading of cryptocurrencies without the need for a central authority. DEXs use smart contracts to automate the process and ensure transparency and security.
2. Lending and borrowing: DeFi lending and borrowing platforms allow users to lend and borrow cryptocurrencies, often with adjustable interest rates. These platforms use smart contracts to automate the lending process and ensure security and transparency.
3. Stablecoins: Stablecoins are cryptocurrencies that are pegged to a stable asset, such as the US dollar, to minimize price volatility. They play a critical role in DeFi by providing a stable medium of exchange for decentralized financial transactions.
4. Yield farming: Yield farming is a practice of lending cryptocurrencies to DeFi protocols to earn interest. It allows users to earn passive income by participating in the liquidity of DeFi protocols.
5. Insurance: DeFi insurance protocols offer insurance coverage for decentralized financial transactions. They use smart contracts to automate the claims process and ensure security and transparency.
6. Predictive markets: Predictive markets are decentralized platforms that allow users to trade on the outcome of events, such as elections or sports games. They use smart contracts to automate the settlement process and ensure transparency and security.
7.Crowdfunding: DeFi crowdfunding platforms allow users to invest in projects, often in exchange for a token that represents a stake in the project. They use smart contracts to automate the investment process and ensure security and transparency.
8. Identity management: DeFi identity management protocols allow users to store and manage their personal information in a decentralized and secure manner. They use blockchain technology to ensure that users have control over their data and that it cannot be tampered with.
9. Asset tokenization: DeFi asset tokenization protocols allow users to convert real-world assets, such as real estate or art, into tokens that can be traded on blockchain networks. They use smart contracts to automate the process and ensure security and transparency.
These use cases demonstrate the diverse range of financial services that DeFi protocols can provide, and they are just a few examples of how DeFi is changing the way we think about and use money
Defi Predictions for Future
The DeFi Coin token price is now sitting at $0.3578, following some impressive upward momentum in recent weeks. Let’s take a closer look at the coin’s long-term prospects, so you know what kind of profits you may expect from a DEFC investment:
End of 2023: The DeFi sector’s development trend is showing no signs of slowing down, which is expected to entice investors away from established financial systems. If this happens, according to our DEFC price projection, the currency may be worth $0.95 by the end of 2023.
End of 2025: In the coming years, DeFi Coin and DeFi Swap will probably keep growing inside the DeFi market, providing a growing number of opportunities for investors to earn returns on their holdings. We predict that the price of DeFi Coin may reach $1.40 by the end of 2025 since the price of DeFi Coin should logically increase along with this expansion.
DeFi is transforming the world of finance and is at the forefront of the decentralized finance revolution. With its secure, transparent and decentralized platform, DeFi has the potential to provide financial services to millions of people who are currently excluded from traditional financial systems. Although DeFi is still in its early stages, the future looks bright and we can expect to see more and more people adopt.
Credits : Greeshma Pillai, CoffeeBeans

DeFi, short for Decentralized Finance, is a movement that aims to offer financial services and products that are open to everyone, without the need for intermediaries. DeFi has taken the world of cryptocurrency by storm and is quickly becoming one of the hottest trends in the industry.

Decentralized finance (DeFi) and its future

Apache Kafka, the open-source, distributed event streaming platform, has been a game-changer in the world of data processing and integration. It has been continuously evolving over the years, offering new features and improvements that make it more scalable, secure, and efficient. The latest release of Apache Kafka, version 3.3, brings a number of exciting changes, including the replacement of Zookeeper and the introduction of a new consensus mechanism called KRaft.
<img src="https://media.coffeebeans.io/blogs/1_SxN1MbaEhoRsIGImLTLs-A.jpg" alt="cover">
The Evolution of Apache Kafka’s Consensus Mechanism
One of the main changes in Apache Kafka 3.3 is the replacement of Zookeeper with a new consensus mechanism called KRaft. Zookeeper has been a critical component of Apache Kafka for many years, providing a centralized configuration service for the platform. However, Zookeeper has been a bottleneck for large-scale, high-throughput deployments, leading to limitations in scalability and reliability.
KRaft is a distributed consensus algorithm that provides strong consistency guarantees while also being highly scalable. Unlike Zookeeper, which operates in a leader-based fashion, KRaft operates in a leaderless fashion, meaning there is no central point of failure. This provides faster and more efficient leader elections and lower latencies, making it a more reliable and scalable solution for Apache Kafka.
Security Enhancements in Apache Kafka 3.3
In addition to the replacement of Zookeeper, Apache Kafka 3.3 also brings a number of security enhancements. This includes support for Mutual TLS (Transport Layer Security), which provides an additional layer of encryption for communication between Kafka clients and brokers. This enhances the overall security of the platform, making it a better choice for sensitive and regulated environments.
Apache Kafka 3.3 also includes improved support for Kerberos authentication, which is a widely used security protocol for secure communication over networks. These enhancements make Apache Kafka even more secure, ensuring that data transmitted through the platform is protected against unauthorized access and tampering.
API Enhancements in Apache Kafka 3.3
Apache Kafka 3.3 brings a number of API enhancements that make it easier for developers to build and integrate the platform into their applications. This includes new APIs for managing topics and partitions, as well as improvements to the existing APIs for producing and consuming messages. These enhancements make the platform more user-friendly and accessible, providing developers with more options for customizing and integrating Apache Kafka into their applications.
The Power of Apache Kafka Connector APIs
Another major feature of Apache Kafka 3.3 is the addition of new connector APIs. These APIs make it easier to integrate Apache Kafka with other systems and data sources, such as databases and data lakes. This opens up a whole new world of possibilities for data ingestion, processing, and analysis, making Apache Kafka an even more powerful tool for building modern data-driven applications.
Conclusion
In conclusion, Apache Kafka 3.3 is a major milestone for the platform, offering a number of exciting new features and enhancements that make it even more scalable, secure, and efficient. From the replacement of Zookeeper with KRaft, to the new security and API enhancements, this release makes Apache Kafka an even better choice for event streaming and data integration. Whether you’re a developer looking to build a new application, or an IT professional looking to modernize your data architecture, Apache Kafka 3.3 is the perfect solution for your needs.
Keywords: Apache Kafka, Zookeeper, KRaft, event streaming, data integration, security, API enhancements, connector APIs, Mutual TLS, Kerberos authentication, distributed consensus algorithm.\
Credits: Greeshma Pillai, <a href="https://coffeebeans.io/">coffeebeans</a>

Apache Kafka, the open-source, distributed event streaming platform, has been a game-changer in the world of data processing and integration. It has been continuously evolving over the years, offering new features and improvements that make it more scalable, secure, and efficient. The latest release of Apache Kafka, version 3.3, brings a number of exciting changes, including the replacement of Zookeeper and the introduction of a new consensus mechanism called KRaft.

Discovering the Innovations in Apache Kafka 3.3: From Zookeeper to KRaft

Asha, one of the finest employees in her organization, fell into a financial crisis a year back, for reasons outside her control. She missed some payments on EMI and her credit score suffered. This is making it difficult for her to get a new job and is worsening her financial situation. Which in turn, is making it even more difficult for her to find a job.
This isn’t the truth, at least as yet. But you know that you are not very far from this reality. The reality where algorithms will start judging you and take more decisions for you. And before you know it, you will be in an Algorithmic Prison.
<img src="https://media.coffeebeans.io/blogs/Algorithm-Prison-1-2048x1152.jpg" alt="cover">
We are increasingly seeing the use of Machine learning algorithms in predicting multiple facets of life. Predicting if it is going to rain tomorrow is one thing, predicting whether a loan applicant will default, is an entirely different proposition. The algorithms are deciding if you should get the loan, if you are fit for a job, or if you are eligible for that insurance. Although, for companies, the loss of missing out on opportunities on risky propositions may not be worth it; for individuals, it could be life-changing and have very high negative repercussions.
To make matters worse, through connected systems, if you are denied one service, it is likely you will be deemed unfit for multiple such opportunities.
The Bias
The algorithms study multiple data points and behavioral aspects of the past and give out probabilistic predictions. If the past was biased, it is very likely the predictions will show that too. Thereby, possibly depriving certain regions and castes, and religions of opportunities. While we morally preach equality for all, the algorithms do not have moral values. At least not as of now.
Moreover, the algorithms make predictions while segmenting you into buckets. If on average, people like you from the same region, company, or race have defaulted, it doesn’t mean you are going to default too
The Polarised Views
If you are consuming a kind of content. the engines show similar content to you in anticipation of increasing click-throughs. Thereby, just adding to the confirmation bias of your opinions and creating polarised views. We are already seeing the impact of social media on election campaigns. And then there are companies, who are trying hard to use those analytics and help political parties. Media is the only source of information for you, and that is highly driven by algorithms on what to show you next. The movie Inception… its already here, just not in the form of the movie.
The Justice
Most of the algorithms often keep learning with data and make decisions on their own. This makes the systems highly opaque and difficult for a human to reason why a particular decision was made.
We have a proper judiciary system in place. But if an algorithm is denying you a job or a loan, who is to be blamed for that? If it is discriminating against a race or religion, can you explain how the algorithm made the decision? Well, the justice system has to evolve. For all you know, that will also be driven by algorithms.
Privacy — Huh?
You have thought of this at least once that Alexas, Siris, and Googles of the world are “listening”, to you. We have no clue how the companies are tracking our behavioral data for the purpose of providing the best recommendations and services possible.
Monika, a friend, through her privacy awareness, has enabled the highest security an app has to provide. But, there are services that are part of our lives now. Choosing complete privacy is not possible now. We all have to be socially connected. She has to use Maps frequently and share her location. Privacy — well, yeah!!!
Do the right thing
AI is the new hype and we are seeing new prediction/recommendation engines emerging every day. All the systems are being built for a reason and the reasons may vary from being as simple as generating more profits, to as serious as the security of a Nation. It is changing our world entirely, but the consequences may reach far beyond the original purpose.
We understand most of the data is about people’s behaviors and identity. We value and respect it with humanity, while consciously taking responsibility for the world that we want to live in.
An algorithm or software is not good or bad in itself. It is important how we use it. It is up to us, the engineers and the organizations in this domain, to make ethical choices, take responsibility, and Do the right thing
1)Machine learning
2)AI
3)Data Science

Asha, one of the finest employees in her organization, fell into a financial crisis a year back, for reasons outside her control. She missed some payments on EMI and her credit score suffered. This is making it difficult for her to get a new job and is worsening her financial situation. Which in turn, is making it even more difficult for her to find a job.

You are in an Algorithmic Prison. And the funny thing is, you don’t even know what it is

One of the challenges that we face while testing the application is testing it on different browsers and environments. We all might have observed that some websites are not properly displayed on some browsers and we just think that the website is broken. But, as soon as you open it on a different browser, the website opens up just fine. Thus, this behavior explains the compatibility of a website with different browsers.
<ul>
<li>Kiran HS, <a href="https://www.coffeebeans.io/">CoffeeBeans</a></li>
</ul>
<img src="https://media.coffeebeans.io/blogs/kiran-blog-tn.jpg" alt="cover">
Each browser interprets the information on the website page differently, so, some browsers may lack the features that your website is trying to show and make your website look broken on that browser.
That being the brief story, I bet you all have figured out the topic of today’s discussion — Cross Browser Testing. So what is cross browser testing?
Cross browser testing is a type of testing that lets you check whether the websites are working as intended when accessed through
<ul>
<li>Different Browser — OS combination</li>
<li>Browsers like — Chrome, Firefox, Safari, Edge, etc</li>
<li>Operating Systems like — Windows, Linux, MAC, etc</li>
</ul>
It’s about shipping releases that are as browser-agnostic as possible, which is key to delivering a uniform user experience on a diverse, ever-growing range of browsers/devices.
As a tester, it is your responsibility to make sure that not only do applications work, but they work for all your users, no matter what browser, device, or additional assistive tools they are using. Now comes the next question –
Why cross-browser testing?
<ol>
<li>To check how the website appears on different browsers</li>
<li>To check how the website works on different browsers ( quite obvious )</li>
<li>To ensure that you’re not alienating a significant part of your target audience–simply because your website does not work on their browser OS.</li>
</ol>
The next question which comes to mind is –
How to perform cross-browser testing?
This can be done in two (obvious) ways
<ol>
<li>Manual Approach (Manually check the functionality on different browsers and platforms, painful process to follow)</li>
<li>Automation Approach (Write one piece of code and run it on different browsers and platform combinations — sounds cool and easy right?)</li>
</ol>
That being said, let’s dive into the automation approach and see how this can be achieved with Selenium, Selenium GRID, and TestNG
Firstly, you will need to set up a Selenium grid to run the tests on different browsers and platforms. For this, you will need a Selenium server standalone jar which can be found in the given link — <a href="https://selenium-release.storage.googleapis.com/index.html?path=3.141%2F">Selenium Server Standalone Jar</a>
Once you have downloaded the jar file, you need to set up the hub and node based on the requirement. For the demonstration purpose, I will create 1 hub and 2 nodes. 1 of the nodes will run firefox as browser-related test cases. The other node will run chrome-related test cases.
Let’s start by creating a hub.
The command to create a node is as follows
java -jar selenium-server-standalone-3.141.59.jar -role hub
Below is the screenshot of the hub looks when it is up and running
<img src="https://media.coffeebeans.io/blogs/0_G_mWZ6m7Q0JdPGC4.png" alt="terminal"> 
Each browser interprets the information on the website page differently, so, some browsers may lack the features that your website is trying to show and make your website look broken on that browser.
That being the brief story, I bet you all have figured out the topic of today’s discussion — Cross Browser Testing. So what is cross browser testing?
Cross browser testing is a type of testing that lets you check whether the websites are working as intended when accessed through
<ul>
<li>Different Browser — OS combination</li>
<li>Browsers like — Chrome, Firefox, Safari, Edge, etc</li>
<li>Operating Systems like — Windows, Linux, MAC, etc</li>
</ul>
It’s about shipping releases that are as browser-agnostic as possible, which is key to delivering a uniform user experience on a diverse, ever-growing range of browsers/devices.
As a tester, it is your responsibility to make sure that not only do applications work, but they work for all your users, no matter what browser, device, or additional assistive tools they are using. Now comes the next question –
Why cross-browser testing?
<ol>
<li>To check how the website appears on different browsers</li>
<li>To check how the website works on different browsers ( quite obvious )</li>
<li>To ensure that you’re not alienating a significant part of your target audience–simply because your website does not work on their browser OS.</li>
</ol>
The next question which comes to mind is –
How to perform cross-browser testing?
This can be done in two (obvious) ways
<ol>
<li>Manual Approach (Manually check the functionality on different browsers and platforms, painful process to follow)</li>
<li>Automation Approach (Write one piece of code and run it on different browsers and platform combinations — sounds cool and easy right?)</li>
</ol>
That being said, let’s dive into the automation approach and see how this can be achieved with Selenium, Selenium GRID, and TestNG
Firstly, you will need to set up a Selenium grid to run the tests on different browsers and platforms. For this, you will need a Selenium server standalone jar which can be found in the given link — <a href="https://selenium-release.storage.googleapis.com/index.html?path=3.141%2F">Selenium Server Standalone Jar</a>
Once you have downloaded the jar file, you need to set up the hub and node based on the requirement. For the demonstration purpose, I will create 1 hub and 2 nodes. 1 of the nodes will run firefox as browser-related test cases. The other node will run chrome-related test cases.
Let’s start by creating a hub.
The command to create a node is as follows
java -jar selenium-server-standalone-3.141.59.jar -role hub
Below is the screenshot of the hub looks when it is up and running
<img src="https://media.coffeebeans.io/blogs/0_Q0WAC0s4LgUU164D.png" alt="terminal">
Once you have registered that node, let’s register another node with firefox browser configurations. The command to do so is as follows
java -Dwebdriver.gecko.driver=”/Users/macbook/Downloads/geckodriver” -jar selenium-server-standalone-3.141.59.jar -role node -hub <a href="http://localhost:4444/grid/register">http://localhost:4444/grid/register</a>
Below is the screenshot of it looks when it is registered
<img src="https://media.coffeebeans.io/blogs/0_CWb2UdLTXEpKqkQc.png" alt="terminal">
Once both the nodes are registered it can be verified by navigating to the grid console. The same can also be verified on the hub console
Hub Console
<img src="https://media.coffeebeans.io/blogs/0_oA_nApXOVaF-rIA1.png" alt="terminal">
Grid console URL is as follows — <a href="http://localhost:4444/grid/console">GRID Console</a>
<img src="https://media.coffeebeans.io/blogs/0_fOZGNwa3mrtlm72W.png" alt="GRID console">
After you have registered both nodes, get the hub URL and append wd/hub at the end. We will use this URL to route the test cases to specific nodes as configured.
Hub Url — <a href="http://localhost:4444/wd/hub/">Hub URL</a>
Now let’s create a maven project and testng.xml file so that we can run the tests parallelly.
Let’s add the selenium and testng dependency
<img src="https://media.coffeebeans.io/blogs/0_IeJ1q2hDcy8dRTh_.png" alt="image">
<img src="https://media.coffeebeans.io/blogs/0_6uKiJSq_bKiP8hyG.png" alt="image">
After you have registered both nodes, get the hub URL and append wd/hub at the end. We will use this URL to route the test cases to specific nodes as configured.
Hub Url — <a href="http://localhost:4444/wd/hub/">Hub URL</a>
Now let’s create a maven project and testng.xml file so that we can run the tests parallelly.
Let’s add the selenium and testng dependency
<img src="https://media.coffeebeans.io/blogs/0_jsHJ65hHx_7ZSKWT.png" alt="code editor">
<img src="https://media.coffeebeans.io/blogs/0_bBBUErzRNXYoTJ9Y.png" alt="code editor">
Now let’s run the test cases parallely and see if the configurations are working
Command to run the test — mvn clean test
Viola, one single test can now be run on 2 different browsers parallelly. Refer to the console for execution results
<img src="https://media.coffeebeans.io/blogs/0_xRAeer4-SBYGvmAD.png" alt="code editor">
For now, the platform in which I am running the test cases is MAC, this can be changed when a different platform is configured like Windows, Linux, etc. TestNG file to run the test cases parallely is as below.
<img src="https://media.coffeebeans.io/blogs/0_ZRKWiFBK8S87v787.png" alt="code editor">
In the same way, safari and edge can also be configured to run the test cases. It depends on the requirement of how many browsers and platforms need to be covered.
That’s all folks, for now, see you guys in the next exciting blog.
Thank you !!

One of the challenges that we face while testing the application is testing it on different browsers and environments. We all might have observed that some websites are not properly displayed on some browsers and we just think that the website is broken. But, as soon as you open it on a different browser, the website opens up just fine. Thus, this behavior explains the compatibility of a website with different browsers.

Cross Browser Testing using Selenium GRID

Transfer Learning works on the idea that a pre-trained model trained in one setting can be utilized in a different setting for a specific task. I will be doing sentiment analysis here by using a pre-trained BERT model using Pre-Trained Model Approach and will be further training it on a custom dataset prepared.
<ul>
<li>By <a href="https://www.linkedin.com/in/lawa-747151117/">Lakshay Wadhwa</a>, <a href="https://coffeebeans.io/">CoffeeBeans</a></li>
</ul>
<img src="https://media.coffeebeans.io/blogs/blog-tn2.png" alt="cover">
<img src="https://media.coffeebeans.io/blogs/0_olcrXmbB3FH7X2_Z.png" alt="traditional ML vs Transfer learning">
Source: Educative.io
Key Motivation for Transfer Learning– If we look at tasks in our everyday life, we don’t necessarily learn everything from scratch. We essentially try to make our understanding of some new task based on our experiences in the past. It’s also fairly difficult to gather a huge amount of data to train a Deep Learning model from scratch. Transfer learning comes very handy in cases where you don’t have much data to train a new model from scratch.
This article is solely focused on the practical implementation of Transfer Learning by using a pre-trained model and we will further train it according to our requirements.
Let’s walk through the script:
We will start by installing pip, sentencepiece, datasets, and transformers.
<img src="https://media.coffeebeans.io/blogs/0_-JS5IJ4Qctw7r-xq.png" alt="code block">
Next, we will define a class where we would write the function for encoding data, in our case, it’s tweets.
For more details, you can read about the different attributes of tokenizer.encode_plus in the following <a href="https://huggingface.co/transformers/v2.11.0/glossary.html">link</a>.
Also, you can go through this <a href="https://huggingface.co/docs/transformers/main_classes/tokenizer">link</a> as well.
<img src="https://media.coffeebeans.io/blogs/0_p_SesQeHh-ez1GSK.png" alt="code block">
The next steps involve defining hyperparameters as well as the model on which we are going to do training.
There is no strictness regarding the model used here, you can choose your own depending on the problem statement. But it’s always fair to check on its performance before you further train it on your custom data.
<img src="https://media.coffeebeans.io/blogs/0_RIIZ2NbdWuu28tap.png" alt="code block">
Next, we should define a relabeling function that actually defines the labels of your classification problem. Here I am taking three categories 0,1 and 2.
<img src="https://media.coffeebeans.io/blogs/0_JjaBh1lHV0gedtjU.png" alt="code block">
Next is converting the data into an appropriate format before we pass it to a trainer.
<img src="https://media.coffeebeans.io/blogs/0_g1gBbEvTZfBDhulu.png" alt="code block">
<img src="https://media.coffeebeans.io/blogs/0_LtHo6TeA0SgBbXYe.png" alt="code block">
The next step is model training and this is the most crucial step of whole process where we will pass the data to pretrained model trainer.
<img src="https://media.coffeebeans.io/blogs/0_r2_k9DiGp_7KANyw.png" alt="code block">
<img src="https://media.coffeebeans.io/blogs/0_uZsRr_lZw7wQRyku.png" alt="code block">
Please see that I have kept epochs to 10 and the Batch size is 8, my model took approx 3hrs for 1 epoch to train since my dataset was large, you can play around here by keeping epochs less or keeping the dataset small, but if you want to train a good model, my suggestion is to use at least8 to 10 epochs for satisfactory results.
Also, make sure to evaluate your results.
<img src="https://media.coffeebeans.io/blogs/0_E778b0GgLXoPMgyR.png" alt="code block">
<h1>The next concept I want to share is ONNX model format.</h1>
ONNX is an intermediary machine learning framework used to convert different machine learning frameworks. In Short, train your custom model in whatever framework you are comfortable with, like Tensorflow and Pytorch, and just finally convert it to common ONNX format. This provides a lot of flexibility to developers as well. You can also use ONNX models with optimized inference frameworks (Onnx runtime, Ncnn, Tvm etc) to maximize performance.
(I will write a separate blog later for ONNX in detail)
According to official documentation :
<img src="https://media.coffeebeans.io/blogs/0_jILR_D1L-6qfsavy.png" alt="Desctiption of ONNX">
<h1>The next concept I want to share is ONNX model format.</h1>
ONNX is an intermediary machine learning framework used to convert different machine learning frameworks. In Short, train your custom model in whatever framework you are comfortable with, like Tensorflow and Pytorch, and just finally convert it to common ONNX format. This provides a lot of flexibility to developers as well. You can also use ONNX models with optimized inference frameworks (Onnx runtime, Ncnn, Tvm etc) to maximize performance.
(I will write a separate blog later for ONNX in detail)
According to official documentation :
<img src="https://media.coffeebeans.io/blogs/0_wkIEzJg18rRoq6of.png" alt="code block">
<img src="https://media.coffeebeans.io/blogs/0_C-vVDnM2HMyXNaV6.png" alt="code block">
Please note here that defining features is really important as this defines for what specific task we are converting our model to ONNX format. The last step involves converting the model into ONNX format and then checking the export or loading the model and then utilizing it in the production or research environment.
Please suggest any changes if some error is there, the error would be rectified immediately.

Transfer Learning works on the idea that a pre-trained model trained in one setting can be utilized in a different setting for a specific task. I will be doing sentiment analysis here by using a pre-trained BERT model using Pre-Trained Model Approach and will be further training it on a custom dataset prepared.

How to use a pre-trained BERT model for classification using Transfer learning

A product roadmap is a document that outlines the steps that will be taken for a product or solution to be developed over time. Roadmaps are used by product owners to specify future product functionality and the timing of the release of new features. When used in agile development, a roadmap gives the team’s daily work critical context and should be adaptable to changes in the market.<img src="https://media.coffeebeans.io/blogs/1_PqG8vJjrHNrcYfLSwAfI4g.png" alt="cover">What is a Product Roadmap?
A product roadmap is a document that outlines the steps that will be taken for a product or solution to be developed over time. Roadmaps are used by product owners to specify future product functionality and the timing of the release of new features. When used in agile development, a roadmap gives the team’s daily work critical context and should be adaptable to changes in the market.
Nobody knew what product roadmaps should look like until recently, but we all knew something had to change.
To communicate our product plans at older companies, we used pen and paper and a release planner. They were long, complicated, and detailed — after all, they were designed for developers — and they resulted in a jumbled backlog that was difficult to follow.
Today, road mapping is a divisive topic, but one thing is clear: It is necessary. We all know that no one reads the lengthy piece. We’re also learning how effective a roadmap can be when it’s so well-designed that teams use it to make product decisions and businesses use it to make business decisions.
We’ll look at how a roadmap can help you connect your work with that of others and reclaim control over your product. But what exactly does that road map entail? Let’s dive in to understand it better.
What does a great product roadmap look like?
A good product roadmap must be visually appealing, easily accessible, and clear enough for anyone to scan for answers to the following questions:
· What exactly are we doing?
· Why are we doing it in the first place?
· What does this have to do with our OKRs?
OKRs are defined as Objectives and Key Results that the product is entitled to achieve through the set of features you are building for the product. It encompasses the milestones you will attain in releasing those features.
A theme-based product roadmap is based on this premise, and the benefits are enormous and immediate.
· Reduce the number of meetings you attend — On the roadmap, your priorities (what and why) are clearly stated. You do not need to explain things to different people in different ways.
· Encourage constructive team debates — Your roadmap can serve as a benchmark for team members to hold themselves and one another accountable for linking deliverables to roadmap goals and OKRs.
· Make product choices, everyone understands — you’re no longer the jerk who dismisses ideas. You can actually discuss customer feedback and ideas using your roadmap and priorities, which are visible to everyone
We must abandon the notion that we can enumerate a set of features that will represent what we will do in the future. This is a ridiculous notion. We should communicate how we will make decisions instead of sharing feature lists with the rest of the company.
A theme-based roadmap is made to be adaptable, so your plans can change as your circumstances change, and it can serve as a communication tool.
o begin, make three columns.
A roadmap, which includes release dates, may be familiar to you. A theme-based roadmap substitute with time horizons made up of three columns:
1. Now — Tasks that you are currently working on
2. Next — Tasks that are coming up soon
3. Later — Tasks that you’d like to work on in the future or need to do a bit more research before you move on
It’s worth noting that we’re not displaying any dates. This isn’t a release schedule; rather, it’s a bird’s eye view of your top priorities. Those are always subject to change, especially in the far future, which you can’t plan for today meaningfully.
The idea is to give yourself some ﬂexibility in case things change. You can push something back if it was current but you no longer want it.
Organize Broad Themes
A roadmap, which includes a list of features and their expected completion date, may be familiar to us.
A theme-based map replaces this with themes, which appear as cards on your roadmap.
Those words come from the realization that almost any product will receive a flood of customer feedback and feature requests that revolve around a single universal problem.
You can either become a slave to those requests or take a step back and address the root of the problem with a single, elegant solution.
Working in themes allows you to think about how you might approach each problem and then decide how to solve it later.
<img src="https://media.coffeebeans.io/1_fCW9nhdBAAmBTa9mEPdYrw.png" alt="image">In user administration in the blue cloud, you can observe it’s considered as them because it consists of certain features that complete that. Sign up for a new account and login for existing users are two basic features that get covered under it. It’s basically a strategic theme to add/login users to an account, not a specific deliverable.
But on a contrary, you can observe that the last one is crossed out. Because you want your cards to be strategic. To be a strategy, “rewriting transactional emails” is far too specific. It’s both an idea and a tactic, and does not belong in the high-level view you’re developing in this step.
It would, however, be more appropriate as an idea within a larger-scale roadmap card.
Inserting Supporting Details
Once you’ve nailed down your themes, you can add more supporting information for anyone who wants to dig deeper. These details help us strengthen what we’re putting on our roadmap, which could include information that’s useful to those who read it:
1. What exactly are we doing?
2. Why are we doing it in the first place?
3. What does this have to do with our OKRs?
OKRs are defined as Objectives and Key Results that the product is entitled to achieve through the set of features you are building for the product. It encompasses the milestones you will attain in releasing those features.
Internally, your team will have access to detailed data that will assist them in preparing for and navigating your workflow. The following are some of the details:
· Ideas — Strategic suggestions for improvement. These concepts respond to a straightforward business question: What problem are you trying to solve?
· Customer feedback — We attach feedback to ideas in Product Board so that it can be linked to potential improvements and we can keep track of what our customers want.
· User stories — For inspiration, consider the following case scenarios: I want to X in order to Y as a user.
Cards in the Future column don’t have to have all of those answers just yet, but they should get a lot more detailed as they get closer to the ‘Current’ term.
Label and Categorize
You can color code and tag your roadmap as you go along to make it easier for the viewer to sort through and filter down based on a specific interest. It’s similar to how you use post-it notes!
Remember, color coding can also help to distinguish certain features based on the parameters you choose like prioritization, efforts, immediate release, customer needs, etc. It simplifies your process of developing features and functionalities for your product.
<img src="https://media.coffeebeans.io/blogs/1_AX2f8QEvpegMdAc6howlyA.png" alt="image">User Profiles, maps of cuisines, and reviews are something most customers look for before going to any restaurant. Similarly, what needs to be built when and for whom, the prioritization should be customer-centric rather than features-centric.
Similarly, you can find next and later features to be developed and in case you want to make changes, make sure it helps to solve your customers’ pain points.
How’s that for basic usability? This keeps things simple and engaging visually, and everyone will appreciate not having to slog through stacks of cards to find what they came for.
Rather than staring blankly at one large roadmap, your co-workers can concentrate on the ones that are relevant to them.
The Bottom Line: This Roadmap Is Much Larger Than You Think
The idea’s sheer brilliance is how, with a single gesture, understanding everything about customers becomes the most influential factor in developing product strategy. “Look at us,” companies say when they talk about features. Take a look at what we’ve accomplished thus far.” “Take a look at what you’re dealing with,” companies say when they discuss customer problems. Take a look at what we’re planning to do to facilitate.”
And, in a single stroke, you’re empowering everyone at your company to think in this manner. You are not the only one. Can you imagine the impact that would have?
Just stick to this famous quote by Jeff Bezos:
References
· <a href="https://happyproject.in/stoic-decisions/">https://happyproject.in/stoic-decisions/</a>
· <a href="https://www.quora.com/What-is-the-better-way-to-build-an-effective-product-roadmap-What-makes-a-good-product-roadmap">https://www.quora.com/What-is-the-better-way-to-build-an-effective-product-roadmap-What-makes-a-good-product-roadmap</a>
· <a href="https://www.prodpad.com/resources/roadmap-course/">https://www.prodpad.com/resources/roadmap-course/</a>
· <a href="https://railsware.com/blog/product-roadmap/">https://railsware.com/blog/product-roadmap/</a>
· <a href="https://productschool.com/blog/product-management-2/product-roadmap-templates/">https://productschool.com/blog/product-management-2/product-roadmap-templates/</a>
· <a href="https://www.smartsheet.com/your-complete-guide-understanding-and-creating-visual-product-roadmaps">https://www.smartsheet.com/your-complete-guide-understanding-and-creating-visual-product-roadmaps</a>
By Rahul Kumar

A product roadmap is a document that outlines the steps that will be taken for a product or solution to be developed over time. Roadmaps are used by product owners to specify future product functionality and the timing of the release of new features. When used in agile development, a roadmap gives the team’s daily work critical context and should be adaptable to changes in the market.

How to Create a Product Roadmap That Everyone Can Understand!

If you are a performance engineer/network engineer or even security engineer, the chance of you encountering eBPF technology in the future is very high. eBPF now has a huge community of users, including big players like Meta, Google, Cloudflare, and Netflix all using this tech in their daily operations.<img src="https://media.coffeebeans.io/blogs/1_PAHzzTuVSwAHRXwIzOFjOw.jpg" alt="cover">
<h2>Prelude</h2>
Let me start the blog with a real story. A year back one of my friends called me to discuss tech (which is a very common thing between us). We share different technical challenges each of us faces at our workplace or by any of our peers and these discussions lead to some informative and creative knowledge-sharing sessions. In such a discussion, he described a specific challenge faced by his cousin, who works for a giant cloud provider. The challenge was to restrict certain IPs dynamically as they provide threats such as a DOS (Denial of service attack), the application developer’s brain in me impetuously replied that these should be handled at a firewall level, or middleware can be written to check the origin of the packet and maintain a blacklist for the malicious senders and ignore the requests (Yes I come from a NodeJS and Go background so the initial solution strikes as a middleware). My friend patiently explained the scale and performance at which this needed to be executed which was way beyond my comprehension. After a noob’s doubt clearing session, we agreed that the scale he wanted could only be achieved at a kernel level. I wished him luck (sarcastically) to write a kernel patch and raise a PR hoping the OS maintainers would include the kernel patch in an upcoming kernel release and he can use this feature when it is released. As a reply to my sarcasm, he shared with me a link to an article that detailed something called “eBPF” (extended Berkeley Packet Filter). I did a basic skimming through the article, and my ignorant mind came to sense that there are amazing inventions in the tech world that I am unaware of.
According to eBPF, you can inject the code directly into the kernel without writing a patch, waiting for it to be approved by the OS maintainer
“RUN YOUR CUSTOM CODE DIRECTLY IN OS KERNEL” — LIZ RICE
“SUPERPOWERS FOR LINUX”. — BRENDAN GREGGS<img src="https://media.coffeebeans.io/0_17I5uYtBRc_AXsy8.png" alt="image">NB: I have added some video and blog links in the reference section please check it out for some amazing sessions and blogs of eBPF.
<h1>History</h1>
The eBPF came to life in 2014, introduced in Linux kernel 3.18, thereby unlocking the God mode of the Linux kernel. The natural doubt anyone reading this blog would have is regarding the name. If this is an “extended berkeley packet filter” then there should be a BPF “berkeley packet filter”. Well, you are right. The BSD packet filter is not a new concept. It was from the 90’s. This gem has been hiding under the radar for years, the Xennails were true innovators. BPF was very basic and its only job was to filter packets at the kernel level hence the name.
NB: I have added the original BPF paper published on December 19, 1992, a very interesting read.
The eBPF has come a long way from BPF, just a packet filtering utility to the consideration of microservices architecture for kernels or as they call it microkernels. All the top tech companies that work at scale nowadays use eBPF on daily basis. CNCF community nowadays breathes and lives on eBPF, if you are a DevOps engineer or sysadmin you would have heard of cilium and Falco both popular in Kubernetes users and production tools that are written on top of eBPF. In 2018 Linux announced it would replace its iptable-based implementation with an eBPF version in the kernel (well replacing iptable with any solution would be better), fall back and disadvantage of using iptables is out of the scope of this article, please go to the reference section and find a well-written article about it. The Kubernetes used iptables for the following use cases mostly
<ul>
<li>Kube-proxy — the component which implements Services and load balancing by DNAT iptables rules</li>
<li>Most CNI plugins are using iptables for Network Policies</li>
</ul>
Cilium has made it more efficient by eliminating the iptable whose performance degrades. 
<ol>
<li></li>
</ol>
Program Execution Bozo’s Guide
To explain the importance of eBPF there needs to be an explanation of how programs are executed in Linux, I will try to explain it from a 1000ft view for everyone.
NB: Windows User? Well why are you even reading this article, you guys do not have all these cool features.
Linux memory is divided into two
<ol>
<li>Kernel space</li>
<li>User space<img src="https://media.coffeebeans.io/blogs/0_ErgxMOK9ov19toqQ.jpg" alt="image">The image itself explains the difference between these two. All the programs that you write are just collections of syscalls that are kernel APIs. Just take the example of opening a file through your favorite programming language that just translates into a fileopen syscalls in the kernel.</li>
<li>When your application asks the kernel for something, a chunk of data in kernel space is frequently copied into user space. We must do this because operating systems strictly partition memory regions used by the kernel, making it impossible to simply provide a pointer to some region of kernel memory to a user space program. This is known as “crossing the user/kernel boundary,” Because of the copy operation, operations like these can have significant performance consequences.</li>
<li>While syscalls cover almost all cases, there arise situations where this is not sufficient like when we need kernel-level performance or write a new driver programming, etc. Depending on the OS maintainers to make patches for all these small use cases is a waste of time and an impossible process. This is where ebpf comes into the picture.</li>
<li>eBPF helps you to write programs in the user space which get packaged and injected into the kernel directly, these programs run on VM in the kernel with a limited instruction set thus extending the capability of the base kernel module.</li>
</ol>
<h1>eBPF Dissected</h1>
eBPF is the provision to run custom code that runs on the kernel for various processes like
<ul>
<li>Observability (tracing)</li>
<li>Debugging</li>
<li>Firewalling</li>
<li>Load Balancing</li>
<li>Network related activity</li>
</ul>
Anyone who has worked in tracing the various programs in the kernel would know its difficulty. The half-baked utilities available in the Linux systems are not enough for profile complex systems or even to extend the perf tooling.
Ebpf is event-driven which means it gets triggered on the following scenarios
<ul>
<li>A system call</li>
<li>Function entry/exit</li>
<li>When a packet enters or leaves</li>
<li>K probes or U probes</li>
</ul>
The programs are written in a language called restricted c which is c with a limited instruction set. The BPF compiler BCC converts this into a bytecode which is loaded into the kernel for execution. A validator is run before compiling to ensure there is no infinite loop or such never-ending I/O operation which could crash the kernel.
<h1>Additional Trick Under Your Sleeve</h1>
The ebpf is indeed a powerful tool that you could have under your sleeve. When working on high-performance projects tweaking the packets or extending the tracing functionality all help you give better observability of what’s happening with the system. Even though encountering the ebpf by an application developer at the present stage is very feeble, if you are a performance engineer/network engineer or even security engineer, the chance of you encountering ebpf in the future is going to shoot up to the sky.
There are some considerations while writing ebpf programs, there have been several privilege escalation attacks that leverage ebpf since it runs in a sudo privilege. The ebpf programs could be used as a powerful aid when leveraging kernel memory vulnerabilities. A detailed writeup of leveraging such a vulnerability was found by Qualys, there is a writeup by them which you can refer to from <a href="https://www.qualys.com/2021/07/20/cve-2021-33909/sequoia-local-privilege-escalation-linux.txt">here</a>.
<h1>Conclusion</h1>
As said in Spiderman movies “Great power comes with great responsibility” when you unlock the God mode of Linux you are on your own, the guards that protected your program from corrupting the whole are not available now. There are specific use cases to use Ebpf, it is not the swiss knife for all your performance issues. The community is pretty huge now including big players like meta, google, Cloudflare, and Netflix all using the tech daily. The tech has loads of potential to grow, recent years have seen separate conf for ebpf enthusiasts.
This blog serves as a small opening to people who are unaware of this cool tech, so please do your research. There are tons of resources available online about ebpf and open source projects being built on top of it. I will be writing a follow-up article detailing how to write a sample ebpf program and execute it.

If you are a performance engineer/network engineer or even security engineer, the chance of you encountering eBPF technology in the future is very high. eBPF now has a huge community of users, including big players like Meta, Google, Cloudflare, and Netflix all using this tech in their daily operations.

eBPF — Divulging The Hidden Super Power

<h1>Appium setup made easy — Step by step process for Installing/ Configuring Appium &#x26; Appium Doctor on macOS for Android platform</h1>
— Vikas Kumar Garg, <a href="https://coffeebeans.io/">CoffeeBeans Consulting</a>
<img src="https://media.coffeebeans.io/blogs/1_c3OH6POKO6PAHEpn0kKENQ.jpg" alt="cover">
<h1>What Is Appium</h1>
Appium is an open-source tool for automating native, mobile web, and hybrid applications on iOS mobile, Android mobile, and Windows Native apps that are written using the iOS, Android, or Windows SDKs.
Mobile web apps are web apps accessed using a mobile browser (Appium supports Safari on iOS and Chrome or the built-in ‘Browser’ app on Android). Hybrid apps have a wrapper around a “web-view” — a native control that enables interaction with web content.
Appium is a “cross-platform” tool: it allows you to write tests against multiple platforms (iOS, Android, Windows), using the same API. This enables code reuse between iOS, Android, and Windows test suites.
<img src="https://media.coffeebeans.io/blogs/0_sKN3XXpV81_7KuD0.png" alt="appium setup">
Appium + Appium Doctor
This article explains in detail how to install &#x26; Configure Appium on MAC with Android studio, related tools, and dependencies for Android.
This is a complete guide so you do not need to do anything separately for configuring Appium for Android on macOS. By using Appium-doctor we will check if our setup is done right. Different sections have been added to the article to explain different components.
<h1>Step-1: Install JAVA JDK</h1>
Java is required to run Appium desktop as well as Appium command line.
You should go to the below link and install JAVA JDK for MAC OS.
<img src="https://media.coffeebeans.io/blogs/1_KmGBWfZ4xDTkdc5ivwLR4g.png" alt="Java SE 11">
<a href="https://www.oracle.com/in/java/technologies/javase/jdk11-archive-downloads.html">https://www.oracle.com/in/java/technologies/javase/jdk11-archive-downloads.html</a>
<h1>Step-2: Set JAVA_HOME</h1>
Appium setup require JAVA_HOME to be set so that it can detect java related requirements while running Appium-Server/ Appium-Doctor.
Open a new terminal window and open .zprofile file by typing: vim .zprofile/ vi .zprofile now type below lines in the file:
export JAVA_HOME=/Library/Java/JavaVirtualMachines/jdk-11.0.13.jdk/Contents/Home
Then, press esc key and enter :wq in the .zprofile to save the file
Open a new terminal and run the below command:
>JAVA_HOME/bin/java -version
You should see this –
<img src="https://media.coffeebeans.io/blogs/0_U8gxJv9y5Ze8Hir5.png" alt="terminal">
Now check JAVA_HOME by typing >echo $JAVA_HOME. You should be able to see below mentioned screenshot :
<img src="https://media.coffeebeans.io/blogs/0_pTxHuk-wU4zf81BS.png" alt="terminal">
<h1>Step-3: Install Android Studio / SDK tools:</h1>
For running Appium tests we need android SDK, Emulators and other related libraries. Android studio provides all these required libraries and emulators as well.
Go to <a href="https://developer.android.com/studio/">https://developer.android.com/studio/</a> and download latest version of android studio.
Launch the .DMG file as installer and the Android Studio Setup Wizard guides you through the rest of the setup, which includes downloading Android SDK components that are required for the setup.
After Opening and selecting the default settings, Android Studio will start downloading the required libraries as shown below.
<img src="https://media.coffeebeans.io/blogs/0_QhjUSgCXjtPkBotM.png" alt="android studio">
<h1>Step-4: Set ANDROID_HOME:</h1>
We should setup all below mentioned environment variables as they are needed by Appium server as well as by Appium-doctor while running the tests and inbuilt applications.
export ANDROID_HOME=/Users/&#x3C;username>/Library/Android/sdk
export PATH=$PATH:$ANDROID_HOME/platform-tools
export PATH=$PATH:$ANDROID_HOME/platform-tools
export PATH=$PATH:$ANDROID_HOME/tools/bin
export PATH=$PATH:$ANDROID_HOME/tools/bin
It looks like this in my machine as shown below in the screenshot
<img src="https://media.coffeebeans.io/blogs/0_MYkc4Kl23Awwybmg.png" alt="terminal">
Now Type this command in the terminal : >android — You should see as shown in this screenshot:
<img src="https://media.coffeebeans.io/blogs/0_iM8xrf7bYwcV-_cd.png" alt="terminal">
You can check the ANDROID_HOME path by typing this command:
>echo $ANDROID_HOME
<img src="https://media.coffeebeans.io/blogs/0_RLh9yqjBIXe783yv.png" alt="terminal">
<h1>Step-5: Install NodeJS and NPM:</h1>
For installing Appium and other Appium related application via terminal we need nodejs and npm.
Check if nodejs is already installed : >node -v
Check if npm is already installed : >npm -v.
If it is installed you will see as shown below in the screenshot
<img src="https://media.coffeebeans.io/blogs/0_hROT5-a4Z-Uaxk6M.png" alt="treminal">
If node and NPM are not installed then go to — <a href="https://nodejs.org/en/download/">https://nodejs.org/en/download/</a> &#x26; download mac installer as shown below.
<img src="https://media.coffeebeans.io/blogs/0_-81SGGumBIuLEuQZ.png" alt="Appium setup GUI">
<img src="https://media.coffeebeans.io/0_mxeAM-Kv51BBJSjx.png" alt="android studio">
<h1>Step-6: Install Appium using NPM via terminal</h1>
<pre><code class="language-shell">> npm install -g appium #installs appium> npm install wd # installs appium web clientIf there is permission issue - use sudo before above commands as shown in the screenshot below.
</code></pre>
<h1>Step-7: Install Appium Desktop</h1>
Appium Desktop is used to configure Appium inspector and other dependencies which are not directly supported in Appium installed via npm. It provides a UI where we can set the dependencies.
Go to this link: <a href="https://github.com/appium/appium-desktop/releases/tag/v1.22.2">https://github.com/appium/appium-desktop/releases/</a> and download dmg installer for mac.
<img src="https://media.coffeebeans.io/blogs/0_GZTjYspmDP5arT2Y.png" alt="terminal">
Click and download .DMG file. For example, “ <a href="https://github.com/appium/appium-desktop/releases/download/v1.22.2/Appium-Server-GUI-mac-1.22.2.dmg">Appium-Server-GUI-mac-1.22.2.dmg</a>“. Double click the .DMG file When the download finishes, and drag and drop the Appium into Applications as shown below.
<img src="https://media.coffeebeans.io/blogs/0_WdxR9RUDNYhnHFzl.png" alt="github">
You should be able to search Appium via spotlight search[command+shift key] in MAC OS as shown below:
<img src="https://media.coffeebeans.io/blogs/0_a89FifbAwSFtqAmU.png" alt="Appium server GUI">
Click on Appium Server GUI → Some people might see the popup as shown below:
<img src="https://media.coffeebeans.io/blogs/0_GFmzgvQV2cPkU88P.png" alt="appiu">
Click ok on the screen and then open system preferences as shown below
<img src="https://media.coffeebeans.io/blogs/0_BxTR1wYMbtJ_Uvnh.png" alt="mac os alert popup">
Click on “Security and Privacy” as highlighted in above screenshot.
You should see as shown below in the screenshot.
<img src="https://media.coffeebeans.io/blogs/0_3IwvfEVEb-MLHIXX.png" alt="settings">
Click on “Open Anyway” in above screenshot and you should see the screen as shown below:
<img src="https://media.coffeebeans.io/blogs/0_U92kfxkGBQT2swnU.png" alt="system preference">
<img src="https://media.coffeebeans.io/blogs/0_kvhlDIcTciYUc-43.png" alt="system preference">
Click on the button “Open” in above screenshot and Appium desktop server startup screen will be shown as displayed below:
<img src="https://media.coffeebeans.io/blogs/0_CnuZH5nI4_EhFWPs.png" alt="mac os popup">
Click on “Start Server v1.22.2” in above screenshot and appium server will start and following screen will appear:
<img src="https://media.coffeebeans.io/blogs/0_MQ0OGM77Ao5TPuJC.png" alt="appium app">
Server will be stopped:
<img src="https://media.coffeebeans.io/blogs/0_AdxSotUobWbIaQhb.png" alt="appium server">
Click on Close Log button in the below screenshot and appium startup page will appear again.
<img src="https://media.coffeebeans.io/blogs/0_KhCG_QqXzG4BgpfJ.png" alt="appium server">
<img src="https://media.coffeebeans.io/blogs/0_S47H2U2KWZhbAACR.png" alt="appium app">
<h1>Step-8: Install Appium-Doctor</h1>
Appium doctor is a command line utility which helps in identifying setup related issues. It helps us in identifying whether our appium setup is proper or not. Appium doctor can be install via terminal using following command(use sudo if you face permission issue)
<img src="https://media.coffeebeans.io/blogs/0_qWfMTtdDg7t9K3MZ.png" alt="appium docker">
> npm install -g appium-doctor
<img src="https://media.coffeebeans.io/blogs/0_eieWK4hYcrRRH-pW.png" alt="terminal">
<h1>Step-9: Run Appium Doctor</h1>
Appium doctor helps us to make sure that our setup is working fine.
Run Appium doctor by following command: > appium-doctor android (For android setup verification). You will see the screen as shown below.
<img src="https://media.coffeebeans.io/blogs/0_yxHI18jcSnkHM5h2.png" alt="terminal">
The above screen confirms that Appium is set up properly for Android.
Stay Tuned for the more insightful blogs

Appium is an open-source tool for automating native, mobile web, and hybrid applications on iOS mobile, Android mobile, and Windows Native apps that are written using the iOS, Android, or Windows SDKs.  Mobile web apps are web apps accessed using a mobile browser (Appium supports Safari on iOS and Chrome or the built-in ‘Browser’ app on Android). Hybrid apps have a wrapper around a “web-view” — a native control that enables interaction with web content.

Installing/Configuring Appium on MAC OS for Android

Linking the customer profile with the features and services to come up with a prioritized and targeted product backlog<img src="https://media.coffeebeans.io/blogs/1_0XX5dG-6s6_X8iE59xldAQ%20(1).jpg" alt="cover">A Product Owner is a team role defined in the Scrum Guide as the team member accountable for maximizing the value of the product resulting from the work of the Team. The work of the Product Owner starts with defining the Product goal and the Product Backlog Items (PBIs) based on inputs from key stakeholders across the organization, market data, competitor analysis and many other factors.
The Product Owner is responsible for defining and prioritising the Product Backlog and also ensuring that it is transparent, visible and understood. For a product to be successful, the market, the team and the product features, all play an important role. From a product owner’s perspective, it is important to ensure that the product developed actually fits the customer values and needs. There may be a good target market and a well designed product but if the product does not fit the market then all the analysis and effort can go to waste.
The Value Proposition Canvas is a tool that can help ensure that a product or service is positioned around what the customer values and their needs. The Value Proposition Canvas, a framework to ensure product-market fit was initially developed by Dr. Alexander Osterwalder.<img src="https://media.coffeebeans.io/blogs/0_SW7hw3psPsE0ikuW.png" alt="image">For a product owner, the Value Proposition Canvas can not only help ensure product-market fit but also be a source for well-structured and prioritised user stories. Let us delve deeper into how the value proposition canvas works and how a product owner can use it efficiently.
As shown above, the Value Proposition Canvas has 2 main components:
<ol>
<li>The SQUARE</li>
<li>The CIRCLE</li>
</ol>
The CIRCLE deals with the customer profile and the SQUARE deals with the value proposition. In simpler words, the CIRCLE talks about the customer, and the SQUARE talks about the product and its features.
For a product owner, the CIRCLE denotes the outcomes or consequences, and the SQUARE denotes what makes the outcomes happen.<img src="https://media.coffeebeans.io/blogs/0_o1n3jrEai47Zo17t.png" alt="image">On the right, the canvas profiles the customer by asking: what jobs are they trying to accomplish, what’s hard or unpleasant (pains) about their current way of doing it, and what’s desirable or delightful (gains) in an ideal solution.
On the left, we map out the value proposition by asking: what is the product or service, and how does it create the gains the customer wants and soothe the pains they don’t.
Now that we know what the two things denote, let us look at how to use them. The PO starts by defining the customer jobs (the functional, social, and emotional tasks customers are trying to perform, problems they are trying to solve, and needs they want to satisfy). These customer jobs help us identify the pains and gains that the customer desires. While it may easily be mistaken, the gains are not the opposite of the pains. The pains are the negative experiences of a customer, while the gains are the benefits that the customer expects and needs.
Let us look at the SQUARE. The Value map (SQUARE) also has 3 components: pain relievers, gain creators, and products and services. The products and services show the features that can help relieve the pain or create gains for the customer, in turn providing value to the customer. The pain relievers detail how the products and services can relieve the pain of the customer, and the gain creators detail how the products and services can offer added value to the customer.
We will now see how all of this ties together. The Gain Creators may provide one or more gains and may also contribute to the relief of one or more of the pains. Similarly, pain relievers can solve one or more pains and may lead to one or more gains for the customer.
For a product owner, if the Gain Creators and Pain Relievers can solve the major pain points and provide gains to the users, and the products and services are taking care of the customer jobs, in other words, if the SQUARE and the CIRCLE are seen as fitting properly with each other, then the product or service is said to be positioned around what the customer values and needs. We can then have a good product-market fit.
Below is the sample Value Proposition Canvas for Uber from back when it was getting off the ground:<img src="https://media.coffeebeans.io/blogs/0_dvqgGDyw-JMOO9bX.png" alt="image">Apart from providing insights into the product-market fit, the Value Proposition Map also provides some other interesting insights for the product owner.
Filling the Gap/Enhance your offering
We talked about how the Value Proposition Canvas tells us how our product features are solving pains and resulting in gains. But if we look at it in reverse order, we observe that the canvas also gives us pains and gains that are not resolved using the current feature set. So, that gives us the GAP in our offering.
We can use this to identify the features that we now want to add that will enhance our offering and make our product more attractive to customers.
Prioritisation
We discover that some Gain Creators will be associated with more than one gain or pain after mapping the relationships between the Gains, Pains, Gain Creators, and Pain Relievers. The same goes for pain relievers. Apart from this, there is an individual priority for the gains and pains as well. These priorities combines with the link between the Value Map (SQUARE) and Customer Profile (CIRCLE), feed into the prioritisation of the Product Backlog by telling us which of the features will provide us a better return on investment.
Writing user Stories
A user story is an articulation of the product backlog items. A user story is the smallest unit of work in an agile framework. It’s an end goal, not a feature, expressed from the software user’s perspective.
As per a recent survey at Thoughtworks, the most preferred format of a user story contains 3 elements: As a____, I want to____, So that____
In this format, we first define the user from whose perspective we want to write the story (as a). We also define the task that the said user wants to accomplish (I want to). Finally, we look at the value that the user wants (so that). This is where the canvas comes in. It provides the gains and pains we want to refer to. A Product Owner would want to ensure that the story leads to pain being relieved or gain being created.
I hope this article helps the readers understand the utility of the Value Proposition Canvas when defining product goals and product backlog. It is a valuable tool in the arsenal of a Product Owner.
Thanks for Reading
Credits: Adyot Kumar Srivastava
Source:
<ol>
<li>CSPO workshop by Lizzy Morris</li>
<li><a href="https://www.scrum.org/resources/what-is-a-product-owner">https://www.scrum.org/resources/what-is-a-product-owner</a></li>
<li><a href="https://www.b2binternational.com/research/methods/faq/what-is-the-value-proposition-canvas/#:~:text=The%20Value%20Proposition%20Canvas%20is,the%20customer%20values%20and%20needs.&#x26;text=It%20is%20a%20detailed%20look,customer%20segments%20and%20value%20propositions">https://www.b2binternational.com/research/methods/faq/what-is-the-value-proposition-canvas/</a></li>
<li><a href="https://www.youtube.com/watch?v=kiWkRiynPAo">https://www.youtube.com/watch?v=kiWkRiynPAo</a></li>
<li><a href="https://interaction.net.au/articles/value-proposition-canvas-explained/">https://interaction.net.au/articles/value-proposition-canvas-explained/</a></li>
<li><a href="https://www.atlassian.com/agile/project-management/user-stories">https://www.atlassian.com/agile/project-management/user-stories</a></li>
</ol>

A Product Owner is a team role defined in the Scrum Guide as the team member accountable for maximizing the value of the product resulting from the work of the Team. The work of the Product Owner starts with defining the Product goal and the Product Backlog Items (PBIs) based on inputs from key stakeholders across the organization, market data, competitor analysis and many other factors.

Value Proposition Canvas — An indispensable tool for Product Owners

So you have an idea? Great. Many people are going to tell you to quickly come up with a prototype or an MVP and validate the acceptance of the idea. You start working on it and a month goes by for your MVP to shape up. Awesome.
What if I tell you that you can start validating your idea the very day the idea strikes you? **Have you heard of Dropbox getting famous without any servers but just with a video? **Well, with my experience I can say that there are many cases when you can confirm your idea without the MVP itself. That’s how I did it.
<img src="https://media.coffeebeans.io/blogs/Get-leaner-1-2048x1152.jpg" alt="cover">Motorpsychos, is a motorcycle renting service in Pune, India. When this idea struck me, there was a dire need for such a service in Pune.
The most deceiving thing about any business idea is that — they all sound great.
Then I made a move, which turned out to be a smart one. Instead of going with the MVP and buying a second-hand motorcycle to see how many users are actually renting, I did something else. It took me one day to create a quick website with a contact-us page. I uploaded information about the service on a few blogs. I registered myself on Justdial, Yellow Pages as a Motorcycle renting agency. I didn’t procure any vehicle.
I just wanted to see if anyone would respond to Motorpsycho’s. To my surprise, on the very 2nd day, I got my first call. But then for a week, nothing happened (I am guessing the 2nd-day call was from a competitor). Suddenly, after one week I started getting 10 to 11 calls a day requesting motorcycles on rent.
The point is if people need something they will find you out. You just have to be easily available.
Way to go. Demand and Market were validated. But I still had doubts about what motorcycles to buy? How much the rent should be? Here are the 5 smart things that I learned without an MVP.
<ol>
<li>I didn’t have any bike to cater to. So when people asked for it, rather than telling them that we don’t have bikes available, I would say “Sorry, we are overbooked and have waitlisted you.” This gave them a feeling that we are doing good and are trusted by others who are renting from me.</li>
<li>I also got an idea of what motorcycles they are interested in renting. If they would ask for a 150 cc vehicle, I would say “It’s not available but I can check the availability of 200 ccs and call you back if you are interested” just to see if they are okay with that. This helped me immensely in decision-making while procuring vehicles.</li>
<li>Before starting the business, I got a fair idea of how much the customer is willing to pay as rent. This gave me a good heads-up with regard to my turnover before I procured a single bike.</li>
<li>I got an idea of what segments of people to target. All the while I was guessing that college-goers will be my major target audience. But to my surprise, there were many working girls too calling for 2-wheelers. I was able to concentrate and focus on my marketing in a better way.</li>
<li>Motorpsychos was already a brand before the business started. This approach wouldn’t have been successful if I had not been extra nice and extra polite to the users who called. I was treating them personally. I followed up with them even if I couldn’t rent them a vehicle, saying “Sorry, you were wait-listed but there were no cancellations, etc.”</li>
</ol>
Having MVP is great, but not always necessary. Remember, the customers always want the moon if you ask them before starting a business. But most of the time, they just want someone to make them feel special and then they will buy whatever you sell.
<ul>
<li>
<a href="https://medium.com/tag/entrepreneurship?source=post">Entrepreneurship</a>
</li>
<li>
<a href="https://medium.com/tag/growth-hacking?source=post">Growth Hacking</a>
</li>
<li>
<a href="https://medium.com/tag/startup?source=post">Startup</a>
Credits: MItesh Bulsara, Serial Entrepreneur and Coach, Chief Technology Officer, CoffeeBeans consulting
</li>
</ul>

So you have an idea? Great. Many people are going to tell you to quickly come up with a prototype or an MVP and validate the acceptance of the idea. You start working on it and a month goes by for your MVP to shape up. Awesome.  What if I tell you that you can start validating your idea the very day the idea strikes you? **Have you heard of Dropbox getting famous without any servers but just with a video? **Well, with my experience I can say that there are many cases when you can confirm your idea without the MVP itself. That’s how I did it.

Get leaner. Don’t waste your time on MVP to validate your idea

Although, this is an old and dusted topic the points I intend to discuss here are based on my 7 years of experience with mobile app development.<img src="https://media.coffeebeans.io/blogs/Hybrid-Vs-Native-App-1-2048x1152.jpg" alt="cover">Hybrid vs Native Battle
Yes, no doubt that a Native app can apparently give a better user experience. And no doubt that if you have budget constraints going native is a challenge. But there are a few points that we need to consider before making the decision. Especially, when there are so many beautiful hybrid app development platforms now.
<ol>
<li>What is the purpose of the app? If the app is a utility and adds to the existing business you are doing, it is worth considering a hybrid app. If the app itself is the main business, think if you really need a native app? I suggest native only if you have to use a lot of native features like GPS, the user experience needs to be kickass, and if you have the money. Most of the banking apps have gone for hybrid, to give you an example.</li>
<li>Cost of ownership. There is no doubt that the cost of development for native is almost double considering multiple platforms. But think about the total lifecycle cost. Each update to the OS, especially with iOS, needs reworking of your code. There might be deprecations, etc. that you will have to fix even if there are no functionality changes. Overall, the cost of maintenance is high.</li>
<li>Time to Market. Hybrid or Mobile app solution is a great way to reach your audience quicker. It’s not really a bad idea to provide MVP with this approach and go native if you see traction. Plus you reach all and not just Android or iOS users.</li>
<li>Enterprise Apps. If you are a company and want to promote BYOD, the Native approach can get expensive if you try making it work for all brands, versions, and different kinds of mobiles unless you force your employees to use one kind of Phone. 😉</li>
</ol>
There are various great Hybrid app development platforms now. Here are a few which are trending:
a. Ionic
b. Titanium
c. Sencha Touch
d. PhoneGap
<ul>
<li><a href="https://medium.com/tag/mobile-app-development?source=post">Mobile App Development</a></li>
<li><a href="https://medium.com/tag/native-vs-hybrid?source=post">Native Vs Hybrid</a></li>
<li><a href="https://medium.com/tag/startup?source=post">Startup</a></li>
</ul>
Credit: Mitesh Bulsara, Serial Entrepreneur and Coach, CTO, CoffeeBeans

This is an old and dusted topic the points I intend to discuss here are based on my 7 years of experience with mobile app development.

Hybrid vs Native: Old Battle — New Approach

Proof of concept (PoC) vs prototyping vs spike!!!!
Frankly to me, all are same and throughout this article, I will stick to the term PoC.<img src="https://media.coffeebeans.io/blogs/Art-of-PoC-2048x1152.jpg" alt="cover">Looking up for definition of PoC I stumbled upon wikipedia which says,
“ Proof of concept (PoC) is a realization of a certain method or idea in order to demonstrate its feasibility, or a demonstration in principle with the aim of verifying that some concept or theory has practical potential.”
Phew!!! That’s a very heavy definition.
To me, PoC is something that proves if stuff works or not. Anything beyond this and the whole fun around PoC gets sucked out.
A new project is about start and it comes with its own package of unknowns. 3rd party integrations? Explore a new graph-based db? Flink vs Spark?
How does one estimate these unknowns? Obviously one can spend an eternity answering these questions but clients really don’t care about your anxieties.
They just care about time. And making blind assumptions without any validation can make or break a project.
That’s where PoCs are invaluable to quickly and efficiently validate our assumptions and gain clarity in regard to making important decisions.
<ul>
<li>Timebox — Based on my experience, if a PoC extends beyond one week (5 working days), then either we are trying to prove too many things as part of this one PoC or the thing we are trying to prove is….not PoC worthy and deserves its own project timeline. PoCs are supposed to be quick and very laser-focused. One week is my rule of thumb.</li>
<li>Document — This aspect is something the developer is oblivious to when he is busy writing code and debugging stuff at 3X speed. But once the PoC is complete and everyone is happy, the learnings are almost every time lost and nobody cares. These learnings can apply to multiple projects and save a lot of time for others and decent documentation (not a very formatted and extensive one) which covers all the learnings is worth saving. It’s not just enough to document the learnings but the hacks and tricks you needed to make for the PoC to work like editing some configuration parameters, tweaking some environment variables, etc. The online resources you have referred to are something worth documenting. I can’t stress enough on this point but documentation is a must.</li>
<li>Write messy working code but not that messy — At least in most of the projects I was involved in, the code used in PoC usually ends up being reused as the base code for the actual project. So it actually makes sense to write some comments and have atleast a basic structure and good naming conventions which can allow others to read and debug the code. I am okay with one gigantic main method but please don’t make the code unreadable with variable names like x1, t2, etc.</li>
<li>Commit the code — Once a PoC is complete it contains not just one java class file but multiple other files like scripts, build files, docker files, etc. Its important to commit all these files in a code repo instead of keeping it on our personal laptop and when asked for the code after 6 months, doing crazy searches across all your drives because as devs we usually end up with file naming conventions like Test1.java, file1.sh, etc.</li>
<li>Demo — Now that you have completed the PoC and discussed the findings with your architect which helped him conclude, you feel satisfied and happy. Want to feel proud, then share the findings with the team as part of a demo. Showcase the whole working thing to the team in a live session and enjoy as others gasp at the magic you have created in just a few days. But it’s not just about you feeling proud. It’s making sure others validate the approach you took and come up with some smart/creative ideas which can take your code to the next level. A discussion can generate many cool ideas as opposed to others reviewing your document in silos.</li>
</ul>
Hope whatever I have covered makes sense and would love to hear others’ thoughts.
Also, please don’t forget to have fun. At the end of the day its awesome to see something running and feel a few points smarter.
<h5>Credits: Sarthak Kumar Barik, Solution Architect</h5>

Looking up for definition of PoC I stumbled upon wikipedia which says, “ Proof of concept (PoC) is a realization of a certain method or idea in order to demonstrate its feasibility, or a demonstration in principle with the aim of verifying that some concept or theory has practical potential.

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