Medallion Lakehouse on Azure
An Azure medallion lakehouse refines data through bronze, silver, and gold Delta layers in ADLS using Databricks, served by Synapse and Power BI. Purview provides catalog and lineage across the tiers.
Medallion Lakehouse on Azure
The medallion architecture organizes a lakehouse into three quality tiers: bronze for raw ingested data, silver for cleaned and conformed data, and gold for business-level aggregates. Each layer improves data quality and is stored as Delta tables in cloud object storage. This Azure implementation pairs Data Lake Storage with Databricks for processing and Synapse for serving. Use it when you need governed, incrementally refined analytical data with clear lineage from source to dashboard.
Components
- Azure Data Lake Storage (ADLS) Gen2: hierarchical object storage holding all medallion layers.
- Azure Data Factory: orchestrates ingestion pipelines from source systems into the bronze zone.
- Azure Databricks: runs the Spark and Delta transformations that promote data through the layers.
- Delta Lake: the ACID table format enabling reliable upserts and time travel on each layer.
- Azure Synapse: serverless SQL pools that serve gold tables to analysts and tools.
- Microsoft Purview: data catalog and lineage across the lakehouse.
- Power BI: dashboards over the gold layer.
Data Flow
Data Factory copies raw files and database extracts into the bronze layer with minimal transformation, preserving source fidelity. Databricks jobs validate, deduplicate, and standardize bronze into silver Delta tables, applying data-quality rules. Aggregation and business logic then build gold tables and dimensional marts. Synapse serverless SQL queries the gold layer directly from ADLS, and Power BI visualizes it. Purview captures lineage across every hop.
Scaling and Resilience
Storage scales independently and cheaply in ADLS. Databricks clusters autoscale per job, and Synapse serverless bills per query scanned. Delta time travel and the transaction log allow safe concurrent writes and rollback after bad loads. Geo-redundant storage and cross-region pipeline replication provide disaster recovery, while idempotent MERGE operations make pipeline retries safe.
Security
ADLS uses Azure AD and POSIX-style ACLs, with encryption at rest and customer-managed keys available. Databricks and Synapse integrate with Azure AD single sign-on and managed identities so no secrets are stored in code. Private endpoints keep traffic off the public internet, and Purview classifies sensitive data to drive masking and access policies.
Trade-offs and Alternatives
The medallion model adds storage and compute for intermediate layers but pays off in debuggability, reprocessing, and clear data contracts. Running both Databricks and Synapse can be operationally heavier than a single engine; some teams serve gold directly from Databricks SQL instead. Alternatives include a Snowflake warehouse with staged loads or Microsoft Fabric's unified OneLake. Choose this design when you want open Delta formats with strong Azure governance and flexible serving options.