Medallion Architecture Explained — Bronze, Silver, and Gold in Plain English
The Medallion Architecture is the most widely used data lake design pattern in 2026. If you are applying for data engineering roles, you will be asked about it in almost every interview. Here is exactly what each layer means, why it exists, and how to implement it on any cloud platform.
Why does the Medallion Architecture exist?
Before the Medallion Architecture, data lakes were a mess. Raw data landed in a single location and everyone — analysts, data scientists, downstream pipelines — read directly from it. The result: inconsistent results, schema surprises, and nobody trusting the data.
The Medallion Architecture solves this by introducing three distinct layers, each with a clear contract about the quality and shape of the data inside it.
Bronze — Raw data, preserved forever
Bronze is the raw layer. Data lands here exactly as it came from the source — no modifications, no cleaning, no transformations. A CSV file lands as a CSV file. JSON from an API lands as JSON.
Why keep raw data? Because mistakes happen downstream. If your Silver transformation has a bug, you need to reprocess from the original source. Bronze is your safety net and audit trail.
Rule: Never modify Bronze data. Append only. Partition by date.
Silver — Clean, validated, and trustworthy
Silver is where cleaning happens. The Silver notebook reads Bronze, applies transformations, and writes back clean Delta Lake data. This includes removing nulls and duplicates, casting strings to proper types, standardizing formats, and adding audit columns.
Silver data is still at row level — not yet aggregated. But it is clean, consistent, and trustworthy. Analysts can query Silver for ad-hoc investigation.
Gold — Aggregated and business-ready
Gold is the final layer. Gold tables are pre-aggregated, optimized for specific business questions, and ready for dashboards without further transformation.
Typical Gold tables: daily sales summary by region and product, customer lifetime value, weekly cohort retention, regional performance rankings.
Gold tables are what Power BI, Looker Studio, and Tableau connect to. They are fast because the heavy aggregation happened during the pipeline run — not at query time.
Implementing Medallion on Azure, AWS, and GCP
The pattern is identical on every cloud — only the service names change.
Azure: ADLS Gen2 (storage) + Databricks (transformations) + Synapse (serving)
AWS: Amazon S3 (storage) + AWS Glue (transformations) + Redshift (serving)
GCP: Cloud Storage (storage) + Dataflow (transformations) + BigQuery (serving)
Learn the architecture pattern first. The cloud services are just implementations of the same idea.