Great points from everyone. In my experience, the choice really hinges on whether you need a unified, metadata-driven access layer or a centralized storage layer for raw and transformed data. That’s essentially the core difference between a data fabric vs data lake approach — fabric focuses on integration and governance across distributed sources, while a lake is optimized for scalable, cost-effective storage and batch/ML workloads.

For teams leaning toward Fabric in the Gold layer, it makes sense if you want tighter T-SQL/Power BI integration and low-code orchestration. If you’re already invested in Databricks and want to minimize duplication, keeping Gold in Databricks and using Serverless SQL for Power BI can be simpler and more cost-efficient.

We’ve explored these trade-offs in more detail in this write-up on data fabric vs data lake, which might help frame the decision based on specific use cases.