The medallion architecture divides a data platform into three layers — bronze, silver, and gold — each with progressively higher data quality. Bronze stores raw, immutable ingested data with metadata. Silver applies validation, deduplication, type casting, and quarantines bad records. Gold contains aggregated, business-ready datasets. A working Python and DuckDB example demonstrates a retail orders pipeline that ingests CSV data, validates and cleans it through the layers, quarantines invalid rows, and produces daily regional sales aggregates. The post also covers idempotent ingestion using file hashes, production considerations like cloud object storage formats (Iceberg, Delta, Hudi), and orchestration tools like Airflow or Dagster.
Table of contents
Where did the medallion architecture come from?What belongs in each layer?What tools do I need to implement a Medallion pattern?A working example: retail orders with Python and DuckDBSummary146 Impressions