Medallion Architecture — Bronze, Silver, Gold

The medallion architecture is a data design pattern that organises a data lakehouse into three progressively refined layers: Bronze (raw, unmodified ingestion), Silver (cleaned and enriched), and Gold (business-level aggregations ready for consumption). Each layer has a specific purpose, quality contract, and consumer profile. The pattern provides clear data lineage, incremental reprocessability (a bug in Silver can be fixed and re-derived from Bronze without re-ingestion), and a separation between data engineering concerns (Bronze and Silver) and business analytics concerns (Gold).

The medallion pattern originated with Databricks' lakehouse architecture and Delta Lake, but applies equally to any table format (Iceberg, Hudi, Parquet) and any cloud storage layer (S3, ADLS, GCS). It maps directly to the dbt three-layer model (staging → intermediate → marts) introduced in Course 4: Bronze corresponds to raw sources, Silver to staging and intermediate models, and Gold to mart models. Teams that have already adopted dbt for SQL transformations are implementing the medallion pattern whether or not they use that terminology.

Analogy🏏Cricket

🏏 Think of it like cricket: This exercise is the IPL official statistics team building their daily automated processing pipeline — the complete workflow that takes raw ball-by-ball records from every ground and produces the certified statistics published on the official website by midnight. Stage 1 is the data catalogue check: verify that the incoming scorecards match the expected format before any processing begins. Stage 2 is the statistics calculation: joins with match metadata, derivation of over-level stats, economy rate computation. Stage 3 is the official record update: load the new statistics into the production database using the certified upsert protocol — delete the old version of today's record and insert the freshly computed one — so no match ever has two records in the official database.