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Editorial draft Data Platforms Lakehouse Delta Lake

Designing a Modern Lakehouse: Bronze, Silver, Gold

How the medallion architecture keeps analytics and ML on the same source of truth — without duplicating storage or governance.

June 14, 2026 8 min read

The lakehouse pattern collapses the historical wall between warehouses and lakes. Instead of copying data from one to the other, we layer transformations on the same open table format.

Why medallion

Bronze captures raw truth. Silver is conformed, deduplicated, joined. Gold is business-shaped — the tables analysts and ML models actually consume.

  • Bronze: append-only, schema-on-read
  • Silver: cleaned, typed, deduped
  • Gold: dimensional, aggregated, contract-first

Table format matters

Delta Lake and Iceberg give us ACID, time travel, and schema evolution on object storage. Pick one and commit — running both doubles your ops surface.

df = (spark.readStream.format('delta').load('/bronze/orders')
  .withWatermark('event_ts', '10 minutes')
  .dropDuplicates(['order_id']))

Governance early, not later

Unity Catalog or Lake Formation from day one. Retrofitting lineage across 400 tables is a career-ending project.

The lakehouse isn't about one engine — it's about one contract.