Medallion Architecture in Databricks: A Complete Implementation Guide

Every data team eventually hits the same wall: raw data scattered across landing zones, inconsistent transformations bolted on over time, and no clear lineage when something breaks at 2 AM. The Med...

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Medallion Architecture in Databricks: A Complete Implementation Guide

Source: DEV Community

Every data team eventually hits the same wall: raw data scattered across landing zones, inconsistent transformations bolted on over time, and no clear lineage when something breaks at 2 AM. The Medallion Architecture exists to solve exactly this — and while the Bronze-Silver-Gold concept is simple, most guides skip the production details that actually matter. This guide goes further. We'll build a complete, production-ready Medallion Architecture implementation in Databricks with PySpark — including schema enforcement, data quality gates, incremental processing, and metadata tracking. What Is the Medallion Architecture? The Medallion Architecture is a data design pattern that organizes your lakehouse into three logical layers: Bronze (Raw) — Ingests data as-is from source systems. Append-only, schema-on-read. Your insurance policy. Silver (Cleaned) — Deduplicates, validates, and conforms data. Schema-on-write with enforced types. Gold (Business) — Aggregated, business-level datasets op

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