Is it necessary to separate semantic tables from the target database, or can all semantic tables be consolidated into a single database? Why might you choose to keep them separate?

In a modern data warehouse, semantic tables—also called business‑level or fact tables—are often kept separate from the raw or target database that holds the source‑level data. The main reason is to decouple the business logic from the underlying data model. By isolating semantic tables, you can evolve the raw schema without impacting downstream analytics, and you can apply business rules, aggregations, and denormalization in a controlled environment. Consolidating all semantic tables into a single database can lead to a cluttered schema, performance bottlenecks, and difficulty in managing access controls. It also makes it harder to version the semantic layer independently. Keeping them separate allows you to version, test, and deploy changes to the semantic layer without touching the raw data, and you can apply different security policies to each layer. In practice, many teams create a dedicated “business” schema or a separate database for semantic tables, while the raw data resides in a “raw” or “bronze” schema. This separation improves maintainability, auditability, and scalability.