Medallion Architecture Explained (Bronze, Silver, Gold)

Medallion architecture isn't a technology you install — it's a discipline. Here's how bronze, silver, and gold layers keep your data platform debuggable. I break down exactly what belongs in each layer: bronze holds raw, append-only source data (your recovery position for replays), silver is clean, typed, conformed data with no business logic, and gold is where business-ready tables and metric definitions like MRR finally live. Then I cover the four rules that keep those layers honest, the five mistakes I see teams make over and over, and — importantly — when you should skip medallion architecture entirely, because it costs you storage and complexity you might not need. The whole payoff is simple: when a number looks wrong, you can walk backwards layer by layer until you find where it broke. ⏱️ Chapters 0:00 - What medallion architecture really is 1:00 - Why one-step pipelines can't be debugged 2:37 - Bronze: raw, append-only source of truth 4:41 - Silver: clean, typed, conformed data 6:31 - Gold: business-ready tables & metrics 8:07 - The 4 rules that keep layers honest 9:36 - 5 mistakes teams make (and the fixes) 11:13 - When to skip medallion entirely 11:54 - Recap: walk a bad number backwards 🔗 Links Databricks: https://databricks.com Snowflake: https://snowflake.com BigQuery: https://cloud.google.com/bigquery #MedallionArchitecture #DataEngineering #BronzeSilverGold #DataPipeline #DataWarehouse #DataLakehouse #Databricks #Snowflake #BigQuery #ETL #ELT #DataModeling #AnalyticsEngineering #DataQuality #ChangeDataCapture