Microsoft Fabric & the Medallion Architecture: One Platform, Three Layers of Trust How one platform and a three-layer pattern can turn scattered systems into one trusted source of truth. Most data teams run orchestration, storage and reporting in separate tools: three subscriptions, three security setups, and copies of data in between. Every copy is a chance for numbers to drift apart. Microsoft Fabric takes a different approach: one platform, one subscription, one copy of data. Here's a quick tour, and how the Medallion architecture fits a typical scenario. Why Fabric? Fabric brings Data Factory, Data Engineering, Data Science, Data Warehouse, Real-Time Intelligence and Power BI together on OneLake: one logical lake for the whole organization, like OneDrive for your data. Two ideas make this practical: Lakehouse and Warehouse. Files and Delta tables for Spark, or a SQL-first engine for structured serving. Both store Delta in OneLake. Shortcuts. Show data from ADLS, S3, Dataverse or another lakehouse as local tables, without copying it. The Medallion architecture in 60 seconds Don't try to make data perfect in one step. Improve it in stages. Bronze (raw): data lands exactly as received, with full history and load metadata. Silver (clean): deduplicated, typed, with conformed keys. Bad rows are set aside. Gold (business-ready): star-schema models, KPIs and aggregates built for Power BI. Follow one employee record: messy in Bronze (padded ID, text date), one clean typed row in Silver, and part of a headcount KPI in Gold. Each layer is a checkpoint you can trace and rebuild. An example scenario: four systems, three departments Picture an organization on Excel, Dataverse, Salesforce and CoreHR, where HR, Operations and Finance pull data by hand. The result: mismatched IDs, copy-paste, and late, unaudited reports. Here's how you could solve it on Fabric: Ingest into Bronze with Data Factory, using the right connector or API for each source. Clean into Silver with PySpark: fix types, remove duplicates, conform IDs. Model into Gold: star schemas with shared dimensions, one per department. Serve with Power BI through Direct Lake, with row-level security per department. Run it from one master pipeline with retries, alerts and the Monitoring hub. Key takeaways Fabric unifies the data stack. OneLake keeps one copy of data. Medallion builds trust layer by layer. Pipelines, Spark and Power BI make the workflow. Start small: one source, three layers. Pick one source in your own environment, decide which Bronze table it becomes, and build outward.
