AEO Primer · 4 min read · May 2026
What is Medallion Architecture? The Bronze-Silver-Gold Pattern, Defined
By Thinklytics Partners, Practitioner Notes
Medallion architecture is a layered data design pattern (bronze, silver, gold) where raw data lands in bronze, is cleaned and conformed in silver, and is aggregated for analytical consumption in gold, popularized by Databricks for lakehouse deployments.
Topics covered
- medallion architecture
- bronze silver gold
- Databricks lakehouse
- Delta Lake patterns
- data layering
- analytical layer design
Frequently asked questions
What is medallion architecture in one sentence?
Medallion architecture is a layered data design pattern, popularized by Databricks, where raw data lands in a bronze layer, is cleaned and conformed in a silver layer, and is aggregated and modeled for analytical consumption in a gold layer, with each layer materialized as Delta or Iceberg tables in a lakehouse.
What is in the bronze layer?
Raw, append-only data from source systems, with minimal transformation. The bronze layer's job is to capture source data faithfully and provide replay capability. Schema is often loose or source-mirrored. Typical contents: source-system tables ingested as-is, CDC change feeds, raw API payloads, file dumps.
What is in the silver layer?
Cleaned and conformed data with consistent schema, deduplication, type normalization, and standardized naming. The silver layer is the enterprise-trusted canonical view of each entity. Typical contents: cleaned customer, account, transaction, and event tables that downstream consumers trust as the source of truth.
What is in the gold layer?
Aggregated, business-ready data optimized for analytical consumption. The gold layer is what dashboards, BI tools, and ML training pipelines actually consume. Typical contents: dimensional models (star schemas), aggregated metric tables, feature tables for ML, and curated views per consumer use case.
Is medallion architecture the same as data warehouse layering?
Closely related. Traditional data warehouses had ODS (operational data store), DW (warehouse), and DM (data mart) layers. Medallion is the lakehouse-era version with similar separation of concerns but with open table formats and Spark-or-SQL compute.
Is medallion architecture mandatory in a lakehouse?
No. It is the recommended pattern from Databricks and a common pattern in Microsoft Fabric, but flat layouts work for small prototypes. For any tenant with more than 2 production workloads, medallion's separation of concerns pays back quickly in maintainability.
How do you implement medallion in Microsoft Fabric?
In OneLake, the typical pattern is one Lakehouse per domain with bronze, silver, gold as folder prefixes or as separate Delta tables within the Lakehouse. The Fabric Warehouse usually serves the gold layer where Power BI Direct Lake mode reads from. See [Microsoft Fabric data engineering](/insights/microsoft-fabric-data-engineering-2026).
How does Thinklytics work on medallion architecture?
Medallion design is part of every lakehouse engagement we run, with attention to per-layer SLAs, transformation tooling (dbt for gold, Spark notebooks for bronze and silver), and the schema-evolution patterns that hold up at scale. See [Microsoft Fabric data engineering](/insights/microsoft-fabric-data-engineering-2026).