When information comes from multiple systems, one database can end up carrying too many responsibilities: preserving history, processing data, and serving business reporting at the same time.
In a LinkedIn post, I described our move toward Fabric data lakes and a medallion architecture. I approach this as an Azure architect: the value is in clear boundaries between technical ingestion and business use.
Bronze: keep the raw record
The first layer focuses on extracting and loading raw, untransformed data from source systems. Instead of putting heavy transformations in front of ingestion, land the data first and preserve a historical record.
This gives you room to revisit the data when business logic changes, without having to recreate the original source history.
Silver: do the processing work
The next layer is the active processing zone. It handles schema validation, deduplication, filtering, and merging multiple sources into a unified dataset.
Keeping that work here separates processing concerns from raw ingestion and from the data people use for analytics.
Gold: make it understandable
The business layer should not ask its users to understand source-system keys or technical database structures. It turns the conformed data into focused, read-optimized datasets that business teams can query and understand.
The point is the boundary
For me, the strongest argument for this architecture is the progression: preserve what came in, make it consistent, then make it useful. Each layer has a clear job, and the business gets a view designed around its needs.
Adapted from my LinkedIn post, “Optimizing the Medallion Processing Loop.” Read the original discussion on the original LinkedIn post.