Managing and processing large datasets efficiently is a key requirement in modern data engineering. Azure Databricks, an optimized Apache Spark-based analytics platform, provides a seamless way to handle such workflows. This blog will explore how PySpark and SQL can be combined to dynamically process, and clean data using the medallion architecture (Only Raw → Silver) and store the results in Azure Blob Storage as PDFs. Understanding the Medallion Architecture: – The medallion architecture follows a structured approach to data transformation: Aggregated Layer (Gold): Optimized for analytics, reports, and machine learning. In our use case, we extract raw tables from Databricks, clean them dynamically, and store the refined data into the silver schema. Key technologies / dependencies used: – Step-by-Step Code Breakdown 1. Setting Up the Environment Install & import necessary libraries The above command installs reportlab, which is used to generate PDFs. This imports essential libraries for data handling, visualization, and storage. 2. Connecting to Azure Blob Storage This snippet authenticates the Databricks notebook with Azure Blob Storage and prepares a connection to upload the final PDFs; Initiates the Spark Session as well. 3. Cleaning Data: Raw to Silver Layer Fetch all raw tables This dynamically removes NULL values from raw data and creates a cleaned table in the silver layer. 4. Verifying and comparing the Raw and the Cleaned (Silver) 4. Converting Cleaned Data to PDFs 5. Converting Cleaned Data to PDFs Output at the Azure Storage Container This process reads cleaned tables, converts them into PDFs with structured formatting, and uploads them to Azure Blob Storage. 6. Automating cleaning at Databricks at fixed scheduleThis is automated by scheduling the notebook & it’s associated compute instance to run at fixed intervals and timestamps. Further actions: – Why Store Data in Azure Blob Storage? To conclude, by leveraging Databricks, PySpark, SQL, ReportLab, and Azure Blob Storage, we have automated the pipeline from raw data ingestion to cleaned and formatted PDF reports. This approach ensures: a. Efficient data cleansing using SQL queries dynamically. b. Structured data transformation within the medallion architecture. c. Seamless storage and accessibility through Azure Blob Storage. This methodology can be extended to include Gold Layer processing for advanced analytics and reporting. We hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudFronts.com

In today’s rapidly evolving AI landscape, organizations are seeking scalable, secure, and efficient ways to deploy intelligent agents. Agent Bricks offers a modular, low-code approach to building AI agents that are reusable, compliant, and production-ready. This blog post explores the evolution of AI leading to Agentic AI, the prerequisites for deploying Agent Bricks, a real-world HR use case, and a glimpse into the future with the ‘Ask Me Anything’ enterprise AI assistant.  Prerequisites to Deploy Agent Bricks  Use Case: HR Knowledge Assistant  HR departments often manage numerous SOPs scattered across documents and portals. Employees struggle to find accurate answers, leading to inefficiencies and inconsistent responses. Agent Bricks enables the deployment of a Knowledge Assistant that reads HR SOPs and answers employee queries like ‘How many casual leaves do I get?’ or ‘Can I carry forward sick leave?’.  Business Impact:  Agent Bricks in Action: Deployment Steps  Figure 1: Add data to the volumes  Figure 2: Select Agent bricks module     Figure 3: Click on Create Agent option to deploy your agent     Figure 4: Click on Update Agent option to update deploy your agent  Agent Bricks in Action: Demo   Figure 1: Response on Question based on data present in the dataset     Figure 2: Response on Question asked based out of the present in the dataset  To conclude, Agent Bricks empowers organizations to build intelligent, modular AI agents that are secure, scalable, and impactful. Whether you’re starting with a small HR assistant or scaling to enterprise-wide AI agents, the time to act is now. AI is no longer just a tool it’s your next teammate. Start building your AI workforce today with Agent Bricks.  We hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudFronts.com Start Your AI Journey Today !!

Introduction: Two Powerful Tools, One Common Question If you work in data engineering, you’ve probably faced this question:Should I use Azure Data Factory or Databricks for my ETL pipeline? Both tools can move and transform data, but they serve very different purposes.Understanding where each tool fits can help you design cleaner, faster, and more cost-effective data pipelines. Let’s explore how these two Azure services complement each other rather than compete. What Is Azure Data Factory (ADF) Azure Data Factory is a data orchestration service.It’s designed to move, schedule, and automate data workflows between systems. Think of ADF as the “conductor of your data orchestra” — it doesn’t play the instruments itself, but it ensures everything runs in sync. Key Capabilities of ADF: Best For: What Is Azure Databricks Azure Databricks is a data processing and analytics platform built on Apache Spark.It’s designed for complex transformations, data modeling, and machine learning on large-scale data. Think of Databricks as the “engine” that processes and transforms the data your ADF pipelines deliver. Key Capabilities of Databricks: Best For: ADF vs Databricks: A Detailed Comparison Feature Azure Data Factory (ADF) Azure Databricks Primary Purpose Orchestration and data movement Data processing and advanced transformations Core Engine Integration Runtime Apache Spark Interface Type Low-code (GUI-based) Code-based (Python, SQL, Scala) Performance Limited by Data Flow engine Distributed and scalable Spark clusters Transformations Basic mapping and joins Complex joins, ML models, and aggregations Data Handling Batch-based Batch and streaming Cost Model Pay per pipeline run and Data Flow activity Pay per cluster usage (compute time) Versioning and Debugging Visual monitoring and alerts Notebook history and logging Integration Best for orchestrating multiple systems Best for building scalable ETL within pipelines In simple terms, ADF moves the data, while Databricks transforms it deeply. When to Use ADF Use Azure Data Factory when: Example:Copying data daily from Salesforce and SQL Server into Azure Data Lake. When to Use Databricks Use Databricks when: Example:Transforming millions of sales records into curated Delta tables with customer segmentation logic. When to Use Both Together In most enterprise data platforms, ADF and Databricks work together. Typical Flow: This hybrid approach combines the automation of ADF with the computing power of Databricks. Example Architecture:ADF → Databricks → Delta Lake → Synapse → Power BI This is a standard enterprise pattern for modern data engineering. Cost Considerations Using ADF for orchestration and Databricks for processing ensures you only pay for what you need. Best Practices Azure Data Factory and Azure Databricks are not competitors.They are complementary tools that together form a complete ETL solution. Understanding their strengths helps you design data pipelines that are reliable, scalable, and cost-efficient. We hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudFronts.com

Most reporting problems do not come from Power BI or visualization tools. They come from how the data is organized before it reaches the reporting layer. A lot of teams try to push raw CRM tables, ERP extracts, finance dumps, and timesheet files directly into Power BI models. This usually leads to slow refreshes, constant model changes, broken relationships, and inconsistent metrics across teams. A clean Medallion Architecture solves these issues by giving your data a predictable, layered structure inside Databricks. It gives reporting teams clarity, improves performance, and reduces rework across projects. Below is a senior-level view of how to design and implement it in a way that supports long-term reporting needs. Why the Medallion Architecture Matters The Medallion model gets discussed often, but in practice the value comes from discipline and consistency. The real benefit is not the three layers. It is the separation of responsibilities: This separation ensures data engineers, analysts, and reporting teams do not step on each other’s work. You avoid the common trap of mixing raw, cleaned, and aggregated data in the same folder or the same table, which eventually turns the lake into a “large folder with files,” not a structured ecosystem. Bronze Layer: The Record of What Actually Arrived The Bronze layer should be the most predictable part of your data platform. It contains raw data as received from CRM, ERP, HR, finance, or external systems. From a senior perspective, the bronze layer has two primary responsibilities: This means storing load timestamps, file names, and source identifiers. The Bronze layer is not the place for business logic. Any adjustment here will compromise traceability. A good bronze table lets you answer questions like:“What exactly did we receive from Business Central on the 7th of this month?”If your Bronze layer cannot answer this, it needs improvement. Silver Layer: Apply Business Logic Once, Use It Everywhere The Silver layer transforms raw data into standardized, trusted datasets. A senior approach focuses on solving root issues here, not patching them later.Typical responsibilities include: This is where you remove all the “noise” that Power BI models should never see. Silver is also where cross-functional logic goes.For example: Once the Silver layer is stable, the Gold layer becomes significantly simpler. Gold Layer: Data Structured for Reporting and Performance (Gold) represents the presentation layer of the Lakehouse. It contains curated datasets designed around reporting and analytics use cases, rather than reflecting how data is stored in source systems. A senior-level Gold layer focuses on: Gold tables should reflect business definitions, not technical ones. If your teams rely on metrics like utilization, revenue recognition, resource cost rates, or customer lifetime value, those calculations should live here. Gold is also where performance tuning matters. Partitioning, Z-ordering, and optimizing Delta tables significantly improves refresh times and Power BI performance. A Real-World Example In projects where CRM, Finance, HR, and Project data come from different systems, reporting becomes difficult when each department pulls data separately. A Medallion architecture simplifies this: The reporting team consumes these gold tables directly in Power BI with minimal transformations. Why This Architecture Works for Reporting Teams To conclude, a clean Medallion Architecture is not about technology – it’s about structure, discipline, and clarity. When implemented well, it removes daily friction between engineering and reporting teams.It also creates a strong foundation for governance, performance, and future scalability. Databricks makes the Medallion approach easier to maintain, especially when paired with Delta Lake and Unity Catalog. Together, these pieces create a data platform that can support both operational reporting and executive analytics at scale. We hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudFronts.com

The Integration Shift No Enterprise Can Ignore For more than a decade, legacy 3rd-party integration platforms served as the backbone of enterprise operations. But in a world being redefined by AI, cloud-native systems, and real-time data, these platforms are no longer keeping pace. Across industries, CIOs and digital leaders are facing the same reality: What was once a dependable foundation has now become a barrier to modern transformation. This is why enterprises around the world are accelerating the shift to Azure Integration Services (AIS) a cloud-native, modular, and future-ready integration backbone. From our field experience including the recent large-scale migration from TIBCO for Tinius Olsen one message is clear: Modernizing integration is not an IT upgrade. It is a business modernization initiative. 1. Why Integration Modernization Is Now a Business Imperative Digital systems are more distributed than ever. AI and automation are accelerating. Data volumes have exploded. Customers expect real-time experiences. Yet legacy middleware platforms were built for a world before: The challenges CIOs consistently report include: • Escalating licensing & maintenance costs: Annual renewals, hardware provisioning, and forced upgrades drain budgets. • Limited elasticity: Legacy platforms require you to over-provision capacity “just in case,” increasing cost and reducing efficiency. • Rigid, code-heavy orchestration: Every enhancement takes longer, requiring specialized skills. • Poor monitoring and operational visibility: Teams struggle to troubleshoot issues quickly due to decentralized logs. • Slow deployment cycles: Innovation slows down because integration becomes the bottleneck. This is why the modernization conversation has moved from “Should we?” to “How soon can we?”. 2. Why Azure Is Becoming the Digital Backbone for Modern Enterprises Azure Integration Services brings together a powerful suite of cloud-native capabilities: This is not a one-to-one replacement for middleware. It is an entirely new integration architecture built for the future. 3. What We Learned from the TIBCO → Azure Migration Journey Across the Tinius Olsen modernization project and similar enterprise engagements, six clear lessons emerged. 1. Cost Optimization Is Real and Immediate Moving to Azure shifts integration from a heavy fixed-cost model to a lightweight consumption model. Clients consistently see: Integration becomes a value driver not a cost burden. 2. Elastic Scalability Gives Confidence During Peak Loads Legacy platforms require expensive over-provisioning. Azure scales automatically depending on demand. The result: Scalability stops being a constraint and becomes an advantage. 3. Observability Becomes a Competitive Advantage Azure’s built-in monitoring ecosystem dramatically changes operational visibility: Tasks that once required hours of log investigations now take minutes.Root-cause analysis speeds up, uptime improves, and teams can proactively govern critical workflows. 4. Developer Experience Improves Significantly Modern integration requires both: Azure enables both through Logic Apps + Functions, enabling teams to build integrations: Developers can finally innovate instead of wrestling with legacy tooling. 5. The Platform Becomes AI- and Data-Ready Migration to Azure doesn’t just replace middleware.It unlocks new modernization pathways: The integration layer becomes a strategic enabler for enterprise-wide transformation. 6. The Strategic Message for CIOs and Digital Leaders Modernizing integration is not simply about technology replacement. It is about: In short: It is about building a future-ready enterprise. Modernizing Integration Is No Longer Optional The next decade will be defined by AI-driven systems, composable applications, and hyper automation.Legacy integration platforms were not built for this future, Azure is. Enterprises that modernize their integration layer today will be the ones that innovate faster, scale smarter, and operate more efficiently tomorrow. Read the Microsoft-published case study:CloudFronts Modernizes Tinius Olsen with Microsoft Dynamics 365 Talk to a Cloud ArchitectDiscuss your integration modernization roadmap in a 1:1 strategy session. I Hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudFronts.com.

Are you struggling to build secure, low-latency APIs on Azure without spinning up expensive always-on infrastructure? Traditional serverless models like the Azure Functions Consumption Plan are great for scaling, but they fall short when it comes to VNET integration and consistent low latency. Enterprises often need to connect serverless APIs to internal databases or secure networks — and until recently, that meant upgrading to Premium Plans or sacrificing the cost benefits of serverless. That’s where the Azure Functions Flex Consumption Plan changes the game. It brings together the elasticity of serverless, the security of VNETs, and latency performance that matches dedicated infrastructure — all while keeping your costs optimized. What is Azure Functions Flex Consumption? Azure Functions Flex Consumption is the newest hosting plan designed to power enterprise-grade serverless applications. It offers more control and flexibility without giving up the pay-per-use efficiency of the traditional Consumption Plan. Key capabilities include: Why This Matters APIs are the backbone of every digital product. In industries like finance, retail, and healthcare, response times and data security are mission critical. Flex Consumption ensures your serverless APIs are always ready, fast, and safely contained within your private network — ideal for internal or hybrid architectures. VNET Integration: Security Without Complexity Security has always been the biggest limitation of traditional serverless plans. With Flex Consumption, Azure Functions can now run inside your Virtual Network (VNET). This allows your Functions to: In short: You can now build fully private, VNET-secure APIs without maintaining dedicated infrastructure. Building a VNET-Secure Serverless API: Step-by-Step Step 1: Create a Function App in Flex Consumption Plan Step 2: Configure VNET Integration Step 3: Deploy Your API CodeUse Azure DevOps, GitHub Actions, or VS Code to deploy your function app just like any other Azure Function. Step 4: Secure Your API How It Compares to Other Hosting Plans Feature Consumption Premium Flex Consumption Auto Scale to Zero ✅ ❌ ✅ VNET Integration ❌ ✅ ✅ Cold Start Optimized ⚠️ ✅ ✅ Cost Efficiency ⭐⭐⭐⭐ ⭐⭐ ⭐⭐⭐⭐ Enterprise Security ❌ ✅ ✅ Flex Consumption truly combines the best of both worlds – the agility of serverless and the power of enterprise networking. Real-World Use Case Example A large retail enterprise needed to modernize its internal inventory API system.They were running on Premium Functions Plan for VNET access but were overpaying due to idle resource costs. After migrating to Flex Consumption, they achieved: This allowed them to maintain compliance, improve responsiveness, and simplify their architecture — all with minimal migration effort. To conclude, in today’s API-driven world, you shouldn’t have to choose between speed, cost, and security. With Azure Functions Flex Consumption, you can finally deploy VNET-secure, low-latency serverless APIs that scale seamlessly and stay protected inside your private network. Next Step:Start by migrating one of your internal APIs to the Flex Consumption Plan. Test the latency, monitor costs, and see the difference in performance. We hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudFronts.com

Building low-latency, VNET-secure APIs with Azure Functions Flex Consumption is only the beginning.The next step toward modernization is setting up a DevOps release pipeline that automatically deploys your Function Apps-even across multiple regions – using Azure CLI. In this blog, we’ll explore how to implement a CI/CD pipeline using Azure DevOps and Azure CLI to deploy Azure Functions (Flex Consumption), handle cross-platform deployment scenarios, and ensure global availability. Step-by-Step Guide: Azure DevOps Pipeline for Azure Functions Flex Consumption Step 1: Prerequisites You’ll need: Step 2: Provision Function Infrastructure Using Azure CLI Step 3: Configure Azure DevOps Release Pipeline Important Note: Windows vs Linux in Flex Consumption While creating your pipeline, you might notice a critical difference: The Azure Functions Flex Consumption plan only supports Linux environments. If your existing Azure Function was originally created on a Windows-based plan, you cannot use the standard “Azure Function App Deploy” DevOps task, as it assumes Windows compatibility and won’t deploy successfully to Linux-based Flex Consumption. To overcome this, you must use Azure CLI commands (config-zip deployment) — exactly as shown above — to manually upload and deploy your packaged function code. This method works regardless of the OS runtime and ensures smooth deployment to Flex Consumption Functions without compatibility issues. Tip: Before migration, confirm that your Function’s runtime stack supports Linux. Most modern stacks like .NET 6+, Node.js, and Python run natively on Linux in Flex Consumption. Step 4: Secure Configurations and Secrets Use Azure Key Vault integration to safely inject configuration values: Step 5: Enable VNET Integration If your Function App accesses internal resources, enable VNET integration: Step 6: Multi-Region Deployment for High Availability For global coverage, you can deploy your Function Apps to multiple regions using Azure CLI: Dynamic Version (Recommended): This ensures consistent global rollouts across regions. Step 7: Rollback Strategy If deployment fails in a specific region, your pipeline can automatically roll back: Best Practices a. Use YAML pipelines for version-controlled CI/CDb. Use Azure CLI for Flex Consumption deployments (Linux runtime only)c. Add manual approvals for productiond. Monitor rollouts via Azure Monitore. Keep deployment scripts modular and parameterized To conclude, automating deployments for Azure Functions Flex Consumption using Azure DevOps and Azure CLI gives you: If your current Azure Function runs on Windows, remember — Flex Consumption supports only Linux-based plans, so CLI-based deployments are the way forward. Next Step:Start with one Function App pipeline, validate it in a Linux Flex environment, and expand globally. For expert support in automating Azure serverless solutions, connect with CloudFronts — your trusted Azure integration partner. We hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfronts.com

Enterprises today are collecting more data than ever before, yet most leaders admit they don’t fully trust the insights derived from it. Inconsistent formats, missing values, and unreliable sources create what’s often called a data swamp an environment where data exists but can’t be used confidently for decision-making.  Clean, trusted data isn’t just a technical concern; it’s a business imperative. Without it, analytics, AI, and forecasting lose credibility and transformation initiatives stall before they start.  That’s where the Medallion Architecture comes in. It provides a structured, layered framework for transforming raw, unreliable data into consistent, analytics-ready insights that executives can trust.  At CloudFront’s, a Microsoft and Databricks partner, we’ve implemented this architecture to help enterprises modernize their data estates and unlock the full potential of their analytics investments.  Why Data Trust Matters More Than Ever  CIOs and data leaders today face a paradox: while data volumes are skyrocketing, confidence in that data is shrinking.  Poor data quality leads to:  In short, when data can’t be trusted, every downstream process from reporting to machine learning is compromised. The Medallion Architecture directly addresses this challenge by enforcing data quality, lineage, and governance at every stage.  What Is the Medallion Architecture?  The Medallion Architecture is a modern, layered data design framework introduced by Databricks. It organizes data into three progressive layers Bronze, Silver, and Gold each refining data quality and usability.  This approach ensures that every layer of data builds upon the last, improving accuracy, consistency, and performance at scale.  Inside Each Layer    Bronze Layer —> Raw and Untouched   The Bronze Layer serves as the raw landing zone for all incoming data. It captures data exactly as it arrives from multiple sources, preserving lineage and ensuring that no information is lost. This layer acts as a foundational source for subsequent transformations.   Silver Layer —> Cleansing and Transformation   At the Silver Layer, the raw data undergoes cleansing and standardization. Duplicates are removed, inconsistent formats are corrected, and business rules are applied. The result is a curated dataset that is consistent, reliable, and analytics ready.   Gold Layer —> Insights and Business Intelligence   The Gold Layer aggregates and enriches data around key business metrics. It powers dashboards, reporting, and advanced analytics, providing decision-makers with accurate and actionable insights.   Example: Data Transformation Across Layers   Layer   Data Example   Processing Applied   Outcome   Bronze   Customer ID: 123, Name: Null, Date: 12-03-24 / 2024-03-12   Raw data captured as-is   Unclean, inconsistent   Silver   Customer ID: 123, Name: Alex, Date: 2024-03-12   Standardization & de-duplication   Clean & consistent   Gold   Customer ID: 123, Name: Alex, Year: 2024   Aggregation for KPIs   Business-ready dataset   This layered approach ensures data becomes progressively more accurate, complete, and valuable.   Building Reliable, Performant Data Pipelines  By leveraging Delta Lake on Databricks, the Medallion Architecture enables enterprises to unify streaming and batch data, automate validations, and ensure schema consistency creating an end-to-end, auditable data pipeline.  This layered approach turns chaotic data flows into a structured, governed, and performant data ecosystem that scales as business needs evolve.  Client Example: Retail Transformation in Action  A leading hardware retailer in the Maldives faced challenges managing inventory and forecasting demand across multiple locations. They needed a unified data model that could deliver real-time visibility and predictive insights.  CloudFront’s implemented the Medallion Architecture using Databricks:  Results:  Key Benefits for Enterprise Leaders  Final Thoughts  Clean, trusted data isn’t a luxury, it’s the foundation of every successful analytics and AI strategy.  The Medallion Architecture gives enterprises a proven, scalable framework to transform disorganized, unreliable data into valuable, business-ready insights.  At CloudFront’s, we help organizations modernize their data foundations with Databricks and Azure delivering the clarity, consistency, and confidence needed for data-driven growth.  Ready to move from data chaos to clarity? Explore our Databricks Services or Talk to a Cloud Architect to start building your trusted analytics foundation today.  We hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfronts.com

Following our unified data architecture implementation using Databricks Unity Catalog, the next step focuses on understanding the advantages and future potential of this Lakehouse-driven ecosystem. The architecture consolidates data from multiple business systems and transforms it into an AI-powered data foundation that will support advanced analytics, automation, and conversational insights. Key Advantages Centralized Governance:Unity Catalog provides complete visibility into data lineage, security, and schema control — eliminating silos. Dynamic and Scalable Data Loading:A single Databricks notebook can dynamically load and transform data from multiple systems, simplifying maintenance. Enhanced Collaboration:Teams across domains can access shared data securely while maintaining compliance and data accuracy. Improved BI and Reporting:More than 30 Power BI reports are being migrated to the Gold layer for unified reporting. AI & Automation Ready:The architecture supports seamless integration with GenAI tools like Genie for natural language Q&A and predictive insights. Future Aspects In the next phase, we aim to:– Integrate Genie for conversational analytics.– Enable real-time insights through streaming pipelines.– Extend the Lakehouse to additional business sources.– Automate AI-based report generation and anomaly detection. For example, business users will soon be able to ask questions like:“How many hours did a specific resource submit in CRM time entries last week?”Databricks will process this query dynamically, returning instant, AI-driven insights. To conclude, the unified Databricks architecture is more than a data pipeline — it’s the foundation for AI-powered decision-making. By merging governance, automation, and intelligence, CloudFronts is building the next generation of data-first, AI-ready enterprise solutions.

At CloudFronts Technologies, we are implementing a Unified Data Architecture powered by Databricks Unity Catalog to bring together data from multiple business systems into one governed, AI-ready platform. This solution integrates five major systems — Zoho People, Zoho Books, Business Central, Dynamics 365 CRM, and QuickBooks — using Azure Logic Apps, Blob Storage, and Databricks to build a centralized Lakehouse foundation. Objective To design a multi-source data architecture that supports:– Centralized data storage via Unity Catalog.– Automated ingestion through Azure Logic Apps.– Dynamic data loading and transformation in Databricks.– Future-ready integration for AI and BI analytics. Architecture Overview Data Flow Summary:1. Azure Logic Apps extract data from each of the five sources via APIs.2. Data is stored in Azure Blob Storage containers.3. Blob containers are mounted to Databricks for unified access.4. A dynamic Databricks notebook reads and processes data from all sources. Each data source operates independently while following a governed and modular design, making the solution scalable and easily maintainable. Role of Unity Catalog Unity Catalog enables lineage, and secure access across teams. Each layer — Bronze (raw), Silver (refined), and Gold (business-ready) — is managed under Unity Catalog, ensuring clear visibility into data flow and ownership. This ensures that as data grows, governance and performance remain consistent across all environments. Implementation Preview:In the upcoming blog, I will demonstrate the end-to-end implementation of one Power BI report using this unified Databricks architecture. This will include connecting the gold layer dataset from Databricks to Power BI, building dynamic visuals, and showcasing how the unified data foundation simplifies report creation and maintenance across multiple systems. To conclude, this architecture lays the foundation for a unified, governed, and scalable data ecosystem. By combining Azure Logic Apps, Blob Storage, and Databricks Unity Catalog, we are enabling a single source of truth that supports analytics, automation, and future AI innovations.