A Databricks Cluster is a group of virtual machines (VMs) in the cloud that work together to process data using Apache Spark.It provides the memory, CPU, and compute power required to run your code efficiently. Clusters are used for: Each cluster has two main parts: Types of Clusters Databricks supports multiple cluster types, depending on how you want to work. Cluster Type Use Case Interactive (All-Purpose) Clusters Used for notebooks, ad-hoc queries, and development. Multiple users can attach their notebooks. Job Clusters Created automatically for scheduled jobs or production pipelines. Deleted after job completion. Single Node Clusters Used for small data exploration or lightweight development. No executors, only one driver node. How Databricks Clusters WorkWhen you execute a notebook cell, Databricks sends your code to the cluster.The cluster’s driver node divides your task into smaller jobs and distributes them to the executors.The executors process the data in parallel and send the results back to the driver.This distributed processing is what makes Databricks fast and scalable for handling massive datasets. Step-by-Step: Creating Your First Cluster Let’s create a cluster in your Databricks workspace. Step 1: Navigate to Compute In the Databricks sidebar, click Compute. You’ll see a list of existing clusters or an option to create a new one. Step 2: Create a New Cluster Click Create Compute in the top-right corner. Step 3: Configure Basic Settings Step 4: Select Node Type Choose the VM type based on your workload. For development, Standard_DS3_v2 or Standard_D4ds_v5 are cost-effective. Step 5: Auto-Termination Set the cluster to terminate after 10 or 20 minutes of inactivity. This prevents unnecessary cost when the cluster is idle. Step 6: Review and Create Click Create Compute. After a few minutes, your cluster will turn green, indicating it is ready to run code. Clusters in Unity Catalog-Enabled Workspaces If Unity Catalog is enabled in your workspace, there are a few additional configurations to note. Feature Standard Workspace Unity Catalog Workspace Access Mode Default is Single User. Must choose Shared, Single User, or No Isolation Shared. Data Access Managed by workspace permissions. Controlled through Catalog, Schema, and Table permissions. Data Hierarchy Database → Table Catalog → Schema → Table Example Query SELECT * FROM sales.customers; SELECT * FROM main.sales.customers; When you create a cluster with Unity Catalog, you will see a new Access Mode field in the configuration page. Choose “Shared” if multiple users need to access governed data under Unity Catalog. Managing Cluster Performance and CostClusters can become expensive if not managed properly. Follow these tips to optimize performance and cost: a. Use Auto-Termination to shut down idle clusters automatically.b. Choose the right VM size for your workload. Avoid oversizing.c. Use Job Clusters for production pipelines since they start and stop automatically.d. Leverage Autoscaling so Databricks can adjust the number of workers dynamically.e. Monitor with Ganglia metrics to identify performance bottlenecks. Common Cluster Issues and Fixes Issue Cause Fix Cluster stuck starting VM quota exceeded or region issue Change VM size or region. Slow performance Too few workers or data skew Increase worker count or repartition data. Access denied to data Missing storage credentials Use Databricks Secrets or Unity Catalog permissions. High cost Idle clusters running Enable auto-termination. Best Practices for Using Databricks Clusters1. Always attach your notebook to the correct cluster before running it.2. Use development, staging, and production clusters separately.3. Keep the cluster runtime version consistent across environments.4. Terminate unused clusters to reduce cost.5. If you use Unity Catalog, prefer Shared clusters for collaboration. To conclude, clusters are the heart of Databricks.They provide the compute power needed to process large-scale data efficiently. Without them, Databricks Notebooks and Jobs cannot run. Once you understand how clusters work, you will find it easier to manage costs, optimize performance, and build reliable data pipelines. 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

Databricks Time Travel is a powerful feature of Delta Lake that allows you to access older versions of your data. Whether you want to debug issues, recover deleted records, compare historical performance, or audit how data changed over time—Time Travel makes it effortless. It’s like having a complete rewind button for your tables, eliminating the fear of accidental updates or deletes. What is Time Travel? Time Travel enables you to query previous snapshots of a Delta table using either VERSION AS OF or TIMESTAMP AS OF. Delta automatically versions every transaction-UPDATE, MERGE, DELETE, INSERT. So, you can always go back to an earlier state without restoring backups manually. This versioning is stored in the Delta Log, making rewind operations efficient and reliable. Why Time Travel Matters (Use Cases) Debugging Pipelines: Quickly check what the data looked like before a bad job ran. Accidental Deletes: Recover records or entire tables. Audit & Compliance: Easily demonstrate how data has evolved. Root Cause Analysis: Compare two versions side by side. Model Re-training: Use historical datasets to retrain ML models. Data Quality Tracking: Validate when incorrect data first appeared. How Delta Stores Versions (Architecture Overview) Delta Lake stores metadata and version history inside the _delta_log folder. Each commit creates a new JSON or checkpoint Parquet file representing table state. When you run a query using Time Travel, Databricks does not rebuild the entire table. Instead, it directly reads the snapshot based on the transaction log. This architecture makes Time Travel extremely fast and scalable—even on very large datasets. Time Travel Commands Query older data: SELECT * FROM table VERSION AS OF 5; SELECT * FROM table TIMESTAMP AS OF ‘2024-11-20T10:00:00’; A. Example: DESCRIBE HISTORY Below is an example of using DESCRIBE HISTORY on a Delta table. B. Querying a Specific Version Here is how you can fetch an older snapshot using VERSION AS OF. C. Restoring a Table You can restore a Delta table to any older version using RESTORE TABLE. Retention Rules Delta keeps older versions based on two configs: `delta.logRetentionDuration` → How long commit logs are stored. `delta.deletedFileRetentionDuration`→ How long old data files are retained. By default, Databricks keeps 30 days of history. You can increase this if your compliance policy requires longer retention. Best Practices – Use Time Travel for debugging pipeline issues. – Increase retention for sensitive or audited datasets. – Use `DESCRIBE HISTORY` frequently during development. – Avoid unnecessarily large retention windows—they increase storage costs. – Use `RESTORE` carefully in production environments. To conclude, time Travel in Databricks brings reliability, auditability, and simplicity to modern data engineering. It protects teams from accidental data loss and gives full visibility into how datasets evolve. With just a few commands, you can analyze, compare, or restore historical data instantly making it one of the most useful features of Delta Lake. 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

As data platforms mature, teams often face a familiar question:Should we continue with classic ETL pipelines, or move to Delta Live Tables (DLT)? Both approaches work. Both are widely used. The real challenge is knowing which one fits your use case, not which one is newer or more popular. In this blog, I’ll break down Delta Live Tables vs classic ETL from a practical, project-driven perspective, focusing on how decisions are actually made in real data engineering work. Classic ETL in Databricks Classic ETL in Databricks refers to pipelines where engineers explicitly control each stage of data movement and transformation. The pipeline logic is written imperatively, meaning the engineer decides how data is read, processed, validated, and written. Architecturally, classic ETL pipelines usually follow the Medallion pattern: Each step is executed explicitly, often as independent jobs or notebooks. Dependency management, error handling, retries, and data quality checks are all implemented manually or through external orchestration tools. This approach gives teams maximum freedom. Complex ingestion logic, conditional transformations, API integrations, and custom performance tuning are easier to implement because nothing is abstracted away. However, this flexibility also means consistency and governance depend heavily on engineering discipline. We implemented a Classic ETL pipeline in our internal Unity Catalog project, migrating 30+ Power BI reports from Dataverse into Unity Catalog to enable AI/BI capabilities. This architecture allows data to be consumed in two ways – through an agentic AI interface for ad-hoc querying and through Power BI for governed, enterprise-grade visualizations. We chose the ETL approach because it provides strong data quality control, schema stability, and predictable performance at scale. It also allows us to apply centralized transformations, enforce governance standards, optimize storage formats, and ensure consistent semantic models across reporting and AI workloads -making it ideal for production-grade analytics and enterprise adoption. Delta Live Tables Delta Live Tables is a managed, declarative pipeline framework provided by Databricks. Instead of focusing on execution steps, DLT encourages engineers to define what tables should exist and what rules the data must satisfy. From an architectural perspective, DLT formalizes the Medallion pattern. Pipelines are defined as a graph of dependent tables rather than a sequence of jobs. Databricks automatically understands lineage, manages execution order, applies data quality rules, and provides built-in monitoring. DLT pipelines are particularly well-suited for transformation and curation layers, where data is shared across teams and downstream consumers expect consistent, validated datasets. The platform takes responsibility for orchestration, observability, and failure handling, reducing operational overhead. In my next blog, I will demonstrate how to implement Delta Live Tables (DLT) in a hands-on, technical way to help you clearly understand how it works in real-world scenarios. We will walk through the creation of pipelines, data ingestion, transformation logic, data quality expectations, and automated orchestration. The Core Architectural Difference The fundamental difference between classic ETL and Delta Live Tables is how responsibility is divided between the engineer and the platform. In classic ETL, the engineer owns the full lifecycle of the pipeline. This provides flexibility but increases maintenance cost and risk. In Delta Live Tables, responsibility is shared: the engineer defines structure and intent, while Databricks enforces execution, dependencies, and quality. This shift changes how pipelines are designed. Classic ETL is optimized for control and customization. Delta Live Tables is optimized for consistency, governance, and scalability. When Classic ETL Makes More Sense Classic ETL is a strong choice when pipelines require complex logic, conditional execution, or tight control over performance. It is well suited for ingestion layers, API-based data sources, and scenarios where transformations are highly customized or experimental. Teams with strong engineering maturity may also prefer classic ETL for its transparency and flexibility, especially when governance requirements are lighter. When Delta Live Tables Is the Better Fit Delta Live Tables excels when pipelines are repeatable, standardized, and shared across multiple consumers. It is particularly effective for silver and gold layers where data quality, lineage, and operational simplicity matter more than low-level control. DLT is a good architectural choice for enterprise analytics platforms, certified datasets, and environments where multiple teams rely on consistent data definitions. A Practical Architectural Pattern In real-world platforms, the most effective design is often hybrid. Classic ETL is used for ingestion and complex preprocessing, while Delta Live Tables is applied to transformation and curation layers. This approach preserves flexibility where it is needed and enforces governance where it adds the most value. To conclude, Delta Live Tables is not a replacement for classic ETL. It is an architectural evolution that addresses governance, data quality, and operational complexity. The right question is not which tool to use, but where to use each. Mature Databricks platforms succeed by combining both approaches thoughtfully, rather than forcing a single pattern everywhere. Choosing wisely here will save significant rework as your data platform grows. Need help deciding which approach fits your use case? Reach out to us at transform@cloudfronts.com

If you’re new to Databricks, chances are someone told you “Everything starts with a Notebook.” They weren’t wrong. In Databricks, a Notebook is where your entire data engineering workflow begins from reading raw data, transforming it, visualizing trends, and even deploying jobs. It’s your coding lab, dashboard, and documentation space all in one. What Is a Databricks Notebook? A Databricks Notebook is an interactive environment that supports multiple programming languages such as Python, SQL, R, and Scala. Each Notebook is divided into cells you can write code, add text (Markdown), and visualize data directly within it. Unlike local scripts, Notebooks in Databricks run on distributed Spark clusters. That means even your 100 GB dataset is processed within seconds using parallel computation. So, Notebooks are more than just code editors they are collaborative data workspaces for building, testing, and documenting pipelines. How Databricks Notebooks Work Under the hood, every Notebook connects to a cluster a group of virtual machines managed by Databricks. When you run code in a cell, it’s sent to Spark running on the cluster, processed there, and results are sent back to your Notebook. This gives you the scalability of big data without worrying about servers or configurations. Setting Up Your First Cluster Before running a Notebook, you must create a cluster it’s like starting the engine of your car. Here’s how: Step-by-Step: Creating a Cluster in a Standard Databricks Workspace Once the cluster is active, you’ll see a green light next to its name now it’s ready to process your code. Creating Your First Notebook Now, let’s build your first Databricks Notebook: Your Notebook is now live ready to connect to data and start executing. Loading and Exploring Data Let’s say you have a sales dataset in Azure Blob Storage or Data Lake. You can easily read it into Databricks using Spark: df = spark.read.csv(“/mnt/data/sales_data.csv”, header=True, inferSchema=True)display(df.limit(5)) Databricks automatically recognizes your file’s schema and displays a tabular preview.Now, you can transform the data: from pyspark.sql.functions import col, sumsummary = df.groupBy(“Region”).agg(sum(“Revenue”).alias(“Total_Revenue”))display(summary) Or, switch to SQL instantly: %sqlSELECT Region, SUM(Revenue) AS Total_RevenueFROM sales_dataGROUP BY RegionORDER BY Total_Revenue DESC Visualizing DataDatabricks Notebooks include built-in charting tools.After running your SQL query:Click + → Visualization → choose Bar Chart.Assign Region to the X-axis and Total_Revenue to the Y-axis.Congratulations — you’ve just built your first mini-dashboard! Real-World Example: ETL Pipeline in a Notebook In many projects, Databricks Notebooks are used to build ETL pipelines: Each stage is often written in a separate cell, making debugging and testing easier.Once tested, you can schedule the Notebook as a Job running daily, weekly, or on demand. Best Practices To conclude, Databricks Notebooks are not just a beginner’s playground they’re the backbone of real data engineering in the cloud.They combine flexibility, scalability, and collaboration into a single workspace where ideas turn into production pipelines. If you’re starting your data journey, learning Notebooks is the best first step.They help you understand data movement, Spark transformations, and the Databricks workflow everything a data engineer need. 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

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

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

Azure Databricks has become the go-to platform for data engineering and analytics, while Power BI remains the most powerful visualization tool in the Microsoft ecosystem. Connecting Databricks to Power BI bridges the gap between your data lakehouse and business users, enabling real-time insights from curated Delta tables. In this blog, we’ll walk through the process of securely connecting Power BI to Databricks, covering both DirectQuery and Import mode, and sharing best practices for performance and governance. Architecture Overview The connection involves:– Azure Databricks → Your compute and transformation layer.– Delta Tables → Your curated and query-optimized data.– Power BI Desktop / Service → Visualization and sharing platform. Flow:1. Databricks processes and stores curated data in Delta format.2. Power BI connects directly to Databricks using the built-in connector.3. Users consume dashboards that are either refreshed on schedule (Import) or query live (DirectQuery). Step 1: Get Connection Details from Databricks In your Azure Databricks workspace:1. Go to the Compute tab and open your cluster (or SQL Warehouse if using Databricks SQL).2. Click on ‘Advanced → JDBC/ODBC’ tab.3. Copy the Server Hostname and HTTP Path — you’ll need these for Power BI. For example:– Server Hostname: adb-1234567890123456.7.azuredatabricks.net– HTTP Path: /sql/1.0/endpoints/1234abcd5678efgh Step 2: Configure Databricks Personal Access Token (PAT) Power BI uses this token to authenticate securely.1. In Databricks, click your profile icon → User Settings → Developer → Access Tokens.2. Click Generate New Token, provide a name and expiration, and copy the token immediately. (You won’t be able to view it again.) Step 3: Connect from Power BI Desktop 1. Open Power BI Desktop.2. Go to Get Data → Azure → Azure Databricks.3. In the connection dialog:   – Server Hostname: paste from Step 1   – HTTP Path: paste from Step 14. Click OK, and when prompted for credentials:   – Select Azure Databricks Personal Access Token   – Enter your token in the Password field. You’ll now see the list of Databricks tables and databases available for import. To conclude, you’ve successfully connected Power BI to Azure Databricks, unlocking analytical capabilities over your Lakehouse. This setup provides flexibility to work in Import mode for speed or Direct Query mode for live data — all while maintaining enterprise security through Azure AD or Personal Access Tokens. 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