Unifying Multi-Source Data on a Medallion Architecture
Bringing data from many source systems into one consistent, trusted platform using bronze, silver, and gold layers.
- Data Engineering
- Data Ingestion
- Medallion Architecture
The challenge
The client’s data lived in many places: different systems, different formats, and different rules about what a “clean” record looks like. Every team that needed a number ended up stitching sources together on its own, which meant slow answers and reports that didn’t agree with each other.
They needed one place where data from all those sources lands, gets cleaned up once, and is then ready for analytics and downstream use.
The approach
We’re building the platform on the medallion pattern, with three layers that each have one clear job.
| Layer | What lands here | Why it exists |
|---|---|---|
| Bronze | Data from each source, kept as it arrived | A faithful, replayable copy, so anything downstream can be rebuilt without going back to the source |
| Silver | Cleaned, validated, and conformed data | One consistent shape across sources, with bad records caught before they spread |
| Gold | Business-ready datasets built for specific uses | Fast, trusted tables that analysts and applications can use directly |
Ingestion is built so that adding a new source means connecting it to the bronze layer and defining how it maps into silver, not redesigning what’s already there.
Why this shape works
- Raw data is kept, so a fix to a transformation can be replayed instead of re-extracted.
- Cleaning and validation happen once, in silver, instead of separately in every report.
- Gold datasets can change with the business without touching how data is ingested.
Our Batch Data Processing accelerator is built around the same layers.
Status
This is an active engagement, so we’re sharing the approach here rather than final results. We’ll update this page as outcomes are confirmed.
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