Our team is moving away from a traditional Data Warehouse toward a Databricks Lakehouse. I keep hearing about the "Medallion Architecture" (Bronze, Silver, Gold layers). I understand the concept, but I’m struggling with the "Silver" layer specifically. What level of cleaning and transformation should happen there versus the "Gold" layer? We want to ensure that our data scientists have access to clean data without it being too aggregated for their models.
3 answers
The Medallion Architecture is all about incremental refinement. The Bronze layer is your "Raw" landing zone—no changes, just the source data. The Silver layer is where the heavy lifting happens: you should handle type casting, null handling, and deduplication here. Crucially, the Silver layer should remain at the same grain as the source. This is the "Single Source of Truth" that data scientists love because it's clean but not "summarized." The Gold layer is where you apply business logic, aggregations, and joins to create "Project-Specific" tables for BI tools like Power BI. By keeping these separate, you ensure that if business logic changes, you only have to re-run the Gold layer without re-cleaning the raw data.
Are you planning to use "Schema Enforcement" at the Silver layer to prevent "dirty" data from breaking your downstream Gold tables?
We use the Silver layer for "Enterprise-wide" joins. If multiple departments need the same joined data, we put it in Silver to avoid redundant logic in Gold.
That’s a great point, Jessica. We actually treat our Silver layer as a "Data Vault" light, which makes auditing much easier for our compliance team.
Brandon, we are definitely looking into Delta Lake's schema enforcement. My main worry is how to handle "Schema Evolution." If a source system adds a new column, I don't want the whole pipeline to crash, but I also don't want it to just ignore the new data. Is there a way to automate the "promotion" of new columns from Bronze to Silver without manual intervention every time? We have over 200 source tables, and manual updates are becoming a bottleneck for our engineering team. How do you balance "strictness" with "agility" in a large-scale environment?