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The medallion architecture is the most widely-used data design pattern in 2026, especially in Azure Databricks and Microsoft Fabric. It uses three layers — Bronze (raw), Silver (cleaned), and Gold (curated) — to progressively refine data from raw ingestion to business-ready analytics. This guide walks through a production-grade implementation on Azure Data Lake Storage Gen2 with Databricks and Delta Lake.
What is the medallion architecture?
A three-layer data design pattern where data flows Bronze → Silver → Gold. Bronze is raw, append-only, source data. Silver is cleaned, deduplicated, conformed. Gold is aggregated, business-level, query-optimized. Each layer uses Delta Lake, providing ACID transactions, schema enforcement, and time travel.
Why Delta Lake?
Delta Lake is the open table format that brings data warehouse features to data lakes. It supports ACID transactions (no more corrupt files from failed jobs), schema enforcement (catches bad data early), time travel (rollback to any point), and unified batch + streaming. It is the default storage in Databricks and Fabric.
Step 1: Set up the storage
Create an Azure Data Lake Storage Gen2 account with hierarchical namespace enabled. Create three containers: bronze, silver, gold. Configure a service principal with Storage Blob Data Contributor on each container. Mount the containers in Databricks using OAuth.
Step 2: Bronze — ingest
Ingest all source data into the bronze layer as-is, partitioned by ingestion date. Use Auto Loader in Databricks for streaming sources (Kafka, Event Hubs) and copy activity in ADF for batch sources (SAP, SQL Server, SaaS APIs). Store as Delta tables partitioned by year/month/day.
Step 3: Silver — clean
Run a Databricks job nightly that reads the bronze tables, applies data quality rules (deduplication, type coercion, null handling), and writes to the silver layer. Use Delta Live Tables (DLT) for declarative pipelines with built-in expectations.
Step 4: Gold — curate
Build the gold layer with business-level aggregations: daily KPIs, customer 360, product funnel, fraud scores. The gold layer is what Power BI, Tableau, and ad-hoc analysts query. It is denormalized, query-optimized, and small enough to scan in seconds.
Step 5: Governance with Unity Catalog
In Databricks, set up Unity Catalog for unified governance: data assets, permissions, lineage, and audit. Tag bronze tables as "raw", silver as "internal", gold as "confidential". Apply column-level and row-level security where needed. Track lineage automatically from the job graph.
Step 6: Testing and CI/CD
Use dbt for transformations in the silver and gold layers. Write unit tests with dbt tests, integration tests with Great Expectations, and CI/CD pipelines in Azure DevOps. Promote code from dev to staging to prod with environment-specific parameters.
Step 7: Monitoring and observability
Connect Databricks job runs to Azure Monitor and Log Analytics. Set up alerts on failure rate, SLA breaches, and data quality failures. Build a dashboard that surfaces key metrics: row counts, freshness, schema drift, and cost per pipeline.
Talk to a Chennai Counsellor
This is Capstone 1 of the SkilBrill Azure Data Engineering Training in Chennai. The 16-week programme covers every layer of the medallion architecture with hands-on labs in ADF, Databricks, Synapse, and Fabric.
Ready to start? Call +91 8610964691, WhatsApp us, or enrol online. Visit us at No 22, 200 Feet Radial Road, Thoraipakkam, Chennai 600097.
