BI Architecture Frameworks

📌 MS Fabric Breakdown # 1: Architecture (How to Build a BI Solution with Microsoft Fabric) Since Fabric’s release in 2023, a lot of Power BI-centric organizations are moving their entire data stack into Fabric. And honestly it makes perfect sense. If you're already spending thousands on Power BI licensing, why not unify your entire data architecture under one platform? You get a single environment for: → Ingestion → Storage (Lakehouse & Warehouse) → Modeling → Dashboarding All in one place and you solve most of your data silos problems. In this first post of the Fabric series, I’m sharing a high-level BI architecture you can use as a framework for your implementation: 1️⃣ 𝐊𝐧𝐨𝐰 𝐘𝐨𝐮𝐫 𝐏𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐞𝐬 Before diving into technical planning, align your BI goals with real business needs. Ask yourself: → Which departments need reporting the most right now? → What KPIs are critical to track in the short term? → Who are the key stakeholders and decision-makers? This helps you focus your resources and deliver impact from day one. 2️⃣ 𝐈𝐝𝐞𝐧𝐭𝐢𝐟𝐲 𝐘𝐨𝐮𝐫 𝐃𝐚𝐭𝐚 𝐒𝐨𝐮𝐫𝐜𝐞𝐬 Now it’s time to map where your data lives. Some typical examples: ⤷ SQL Server & on-prem databases ⤷ ERPs (SAP, Oracle, etc.) ⤷ SaaS platforms (Salesforce, HubSpot, Stripe, etc.) ⤷ Excel files & manual spreadsheets And remember to prioritize what’s valuable to the business. Don’t waste time and resources ingesting data no one uses. 3️⃣ 𝐌𝐚𝐩 𝐎𝐮𝐭 𝐘𝐨𝐮𝐫 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 Once you know your sources, you need to design the data platform. There are many schools of thought on how to build modern BI architecture. But one of the most practical and scalable is the Medallion Architecture, built across three layers: 1) Bronze Layer: Raw data in its original format 2) Silver Layer: Cleaned, structured, and business-ready tables 3) Gold Layer: Modeled datasets optimized for reporting In Fabric, you can easily orchestrate everything using Data Factory (equivalent of ADF if you're familiar with Azure Ecosystem) and store in OneLake, using Lakehouses and Warehouses depending on your use case. 4️⃣ 𝐂𝐫𝐞𝐚𝐭𝐞 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐬 𝐟𝐨𝐫 𝐒𝐞𝐥𝐟-𝐒𝐞𝐫𝐯𝐢𝐜𝐞 Your final goal is not just storage. It's decision-making. You can build reusable semantic models with KPIs and business logic defined upstream (vs PBI Desktop). This enables business users to explore, visualize, and analyze data without engineering support. Power BI then becomes a front-end for exploration not just a reporting tool. Next in the series: We’ll break down each layer (Bronze → Silver → Gold) with practical examples and tips and how Power BI fits within this architecture. 📥 Save this post if you’re planning to implement Fabric. #MicrosoftFabric #PowerBI #BusinessIntelligence

𝗘𝗺𝗽𝗼𝘄𝗲𝗿𝗶𝗻𝗴 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝘄𝗶𝘁𝗵 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗙𝗮𝗯𝗿𝗶𝗰 & 𝗠𝗲𝗱𝗮𝗹𝗹𝗶𝗼𝗻 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 Excited to share my experience implementing a robust data solution using Microsoft Fabric and the Medallion architecture! Here’s how we structured our end-to-end data pipeline—all within 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗙𝗮𝗯𝗿𝗶𝗰: 𝗦𝗼𝘂𝗿𝗰𝗲 𝗘𝗧𝗟 𝘁𝗼 𝗕𝗿𝗼𝗻𝘇𝗲 𝗟𝗮𝘆𝗲𝗿: Data is ingested from various sources, including on-premises systems, directly into the Bronze layer, ensuring raw data is securely landed and stored. 𝗕𝗿𝗼𝗻𝘇𝗲 → 𝗦𝗶𝗹𝘃𝗲𝗿 → 𝗚𝗼𝗹𝗱: Through Fabric’s powerful data engineering tools, data is incrementally refined: -- 𝗕𝗿𝗼𝗻𝘇𝗲: Raw, unfiltered data. -- 𝗦𝗶𝗹𝘃𝗲𝗿 : Cleaned and enriched data, ready for analytics. -- 𝗚𝗼𝗹𝗱: Curated, business-ready datasets optimized for reporting and insights. 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴: Leveraging Fabric’s semantic modeling capabilities, we define business logic, measures, and relationships, making data meaningful and accessible. 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻: The journey culminates in Power BI, where users interact with dynamic dashboards and reports, unlocking actionable insights from trusted, governed data. 𝗞𝗲𝘆 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀: Unified experience everything runs natively in Fabric Seamless integration from source to insights Scalable, governed, and future-ready analytics If you’re looking to modernize your data platform or unlock the true value of your data, Microsoft Fabric with Medallion architecture is a game changer! #MicrosoftFabric  #GlobalFabricDay #CopilotInFabric #AIinFabric #MedallionArchitecture  #PowerBI  #DataEngineering  #DataPlatform #Analytics #CapacityPlanning #CostOptimization #DataCommunity #TorontoTech #RealTimeAnalytics #OneLake #DataDNA

The days of physically copying data between your Bronze, Silver, and Gold layers might be coming to a close. The advancement of modern open table formats (like #DeltaLake, Apache #Iceberg, and Apache Hudi) and cloud-native platforms (like #Microsoft #OneLake, or analogous services on AWS and Google Cloud) are fundamentally transforming the traditional Medallion architecture. 🔍 What is Medallion Architecture? Medallion architecture is a layered approach to organizing data in a #lakehouse environment, typically with three zones: 🥉 Bronze (Raw) – raw, ingested data from source systems; minimally processed. 🥈 Silver (Cleansed) – cleaned, deduplicated, typed data, ready for analytics. 🥇 Gold (Business) – aggregated and business-friendly datasets used in reporting. Each layer traditionally involves physically copying and transforming data from one zone to the next, often writing new files at each stage. ⚙️ Why is this changing? With metadata-driven open table formats like Delta Lake, Iceberg, and Hudi, updates no longer mutate existing physical data files. Instead, they append new immutable Parquet files and update the transactional metadata, which enables: * Time travel * Versioning * Logical views instead of physical copies ✅ Microsoft’s Perspective Post-OneLake Microsoft promotes a shift from physical zones to logical layers, especially in #MicrosoftFabric with #DeltaLake: “With Delta Lake and #OneLake, you can keep a single physical copy of your #data and access it from different engines and personas. Use shortcuts and views to avoid unnecessary copies.” ✳️ Key recommendations from Microsoft’s vision: * Store once in Delta format (OneLake as the unified storage layer). * Use semantic layers (views, notebooks, Power BI models) to define Silver/Gold logic. * Minimize data movement prefer metadata changes and logical abstractions. * Enable cross-domain access using shortcuts — logical pointers, not copies. * Leverage Direct Lake for fast BI, skipping traditional warehouse ingestion. We’re moving toward a metadata-driven, agile data architecture where logical layering replaces physical duplication, still honoring the principles of #MedallionArchitecture, but rethinking the implementation.