Medallion Architecture: More Than Just Bronze, Silver & Gold

When organizations build a modern data platform, one of the most widely used patterns is Medallion Architecture. But what actually happens in each layer?

🥉Bronze — Raw Data Layer

The goal is to preserve data as it arrives from the source. Sources can include ERP & CRM systems, APIs, SaaS applications, Kafka / streaming, IoT, files, and CDC databases.

• Ingestion
• Schema capture
• Metadata & audit information
• Incremental/CDC processing
• Minimal transformation

Key principle: Keep the source data reproducible and auditable. If business logic changes later, Bronze allows you to reprocess the data without repeatedly extracting everything from the source.

🥈Silver — Clean & Conformed Data

This is where raw data becomes trusted data.

• Data cleansing
• Deduplication
• Data validation
• Schema enforcement
• Data type standardization
• CDC handling
• Joining multiple sources
• Business-rule validation

Silver creates standardized, reusable data for analytics, ML, and downstream data products.

🥇Gold — Business-Ready Data

Gold converts technical datasets into business-focused information — revenue analytics, risk analytics, customer analytics, executive KPIs, ML features, and AI/RAG datasets.

Instead of exposing raw technical fields, Gold can provide meaningful business concepts such as Revenue, Customer Lifetime Value, Claim Severity, and Churn Rate.

The question Gold should answer is: what business decision can this data support?

Why is Medallion useful?

It creates a controlled progression: Raw → Clean → Trusted → Business Ready.

• Data quality
• Reprocessing
• Governance
• Lineage
• Maintainability
• Separation of responsibilities
• Batch + streaming pipelines
• Analytics & AI consumption

But here is the important architecture point: Medallion is not the only data architecture. It is a data refinement pattern that can work together with other approaches — which leads to the next question: should you use Medallion, Data Vault, Dimensional Modeling, Data Mesh, or Lakehouse? That's where architecture decisions become interesting.