We model data in Medallion architecture
Every platform we build organizes data into three layers, Bronze, Silver and Gold, each one cleaner and more useful than the last. It gives every dataset a clear path from raw source to business-ready, and it's what makes data trustworthy enough for analytics and AI.
From raw to ready
Raw
Data lands exactly as it arrived from the source.
- Full history, append-only, never edited
- Source metadata: where it came from and when
- Lets you reprocess anything if rules change
Cleaned & conformed
Data is cleaned, de-duplicated, validated and joined across sources.
- Consistent keys, types and naming
- Data-quality checks and tests
- Sensitive fields masked or de-identified
Business-ready
Data is modeled around how the business asks questions.
- Dimensional models, metrics and aggregates
- Feeds dashboards, apps and AI agents
- Documented, governed and trusted
Why we build this way
Trust
Quality checks at each layer mean the numbers in Gold can be traced back to the source.
Flexibility
Because Bronze keeps the raw history, business rules can change without re-extracting data.
Security
Sensitive data like PHI and compensation is protected in Silver, before anyone downstream sees it.
AI-ready
AI agents and models work from Gold data that's clean, documented and governed.
What it looks like on real projects
Workday
- Bronze
- Raw worker, job and organization extracts from Workday
- Silver
- One clean record per worker, with conformed job and org history
- Gold
- Headcount, movement and attrition models for HR analytics and AI
Healthcare
- Bronze
- Raw HL7 messages, FHIR resources and claims files
- Silver
- Standardized patients, encounters and claims, with PHI protected
- Gold
- Quality measures, utilization and cost models
Cloud migration
- Bronze
- Tables copied as-is from the on-premises database
- Silver
- Validated, reconciled and converted to cloud-native types
- Gold
- Re-modeled for analytics and reporting on the new platform
Works on the platform you choose
- Databricks Delta Lake tables for each layer, governed with Unity Catalog.
- Snowflake Separate schemas or databases per layer, with dynamic tables and tasks between them.
- Microsoft Fabric Lakehouses per layer in OneLake, with pipelines and notebooks moving data through.
- AWS and Azure The same pattern on S3 or Azure Data Lake Storage, with Glue or Data Factory.
Want a data platform built this way?
We'll look at your sources and sketch the Bronze, Silver and Gold layers with you.