Fabric supports several built-in analytical data stores, such as SQL databases, Warehouses, Lakehouses, and Eventhouses, each optimized for different workloads. Microsoft Learn
- SQL databases in Fabric provide a T-SQL relational engine well suited for structured data at moderate volumes (GB–TB), supporting transactional consistency, high-frequency updates, stored procedures, referential integrity, and granular access controls (object-, column-, row-level). Microsoft Learn
- They offer low-latency queries and highly selective lookups, ideal for operational or metadata workloads and scenarios where quick, transactional-style interactions are needed. Microsoft Learn
- Because of automatic integration with Fabric’s underlying lake storage (OneLake), Fabric SQL databases are natively part of the unified Fabric ecosystem — meaning data can be shared across compute engines (e.g., Spark, Warehouse), used in cross-database queries, or consumed by Power BI semantic models / Direct Lake. Microsoft Learn
- In contrast, traditional on-prem or Azure SQL databases are optimized for transactional workloads, but are not integrated with Fabric’s broader data lake architecture, and may not deliver the same flexibility, scalability, or governance when supporting analytics, BI, and mixed workloads.
In short: Fabric SQL databases exist because they combine relational (transactional + structured) capabilities with seamless integration into the unified, scalable, lake-backed Fabric analytics platform — enabling small/moderate data workloads, quick lookups and updates, and smooth interoperability with Warehouses, Lakehouses, Spark, governance, and BI tools