Medallion Architecture: Bronze, Silver, Gold
What belongs in each layer, which rules apply where, and how the medallion pattern maps to raw, curated and published zones.
DatabricksBeginner1 min readUpdated 11 Oct 2026
Three layers
| Layer | Contains | Rules |
|---|---|---|
| Bronze | Source data as received, plus ingestion metadata | Append-only, minimal parsing, keep everything |
| Silver | Cleaned, typed, deduplicated, conformed entities | Enforce schema and quality; one row per business key |
| Gold | Business aggregates and dimensional models | Shaped for dashboards, ML features and APIs |
bronze.orders_raw → silver.orders → gold.daily_sales_by_storeWhy it works
- Replay: Bronze keeps the original data, so Silver and Gold can be rebuilt after a bug fix.
- Separation: Quality rules live in one place (Silver) instead of in every report.
- Clear ownership: Data engineers own Bronze and Silver; analytics engineers often own Gold.
Typical Silver step (Delta)
(spark.read.table("bronze.orders_raw")
.filter("order_id IS NOT NULL")
.withColumn("amount", F.col("amount").cast("decimal(18,2)"))
.dropDuplicates(["order_id", "updated_at"])
.write.mode("overwrite").saveAsTable("silver.orders"))In production, Silver is usually updated incrementally with MERGE rather than overwritten.
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