To build a Fabric analytical data store that supports a relational schema for SQL queries and stores data on OneLake, which option fits?

To build a Fabric analytical data store that supports a relational schema for SQL queries and stores data on OneLake, which option fits?

• A. SQL Endpoint
• B. Lakehouse
• C. Warehouse
• D. KQL database

Answer: (C)

This option fits because the Warehouse in Fabric is the dedicated relational compute layer designed for SQL workloads. It provides a true relational data store where you can create and manage tables with a relational schema and run standard SQL queries, all while storing the data in OneLake. This aligns precisely with the goal of a Fabric analytical data store that supports SQL against a relational schema and uses OneLake for storage. The Lakehouse is broader, unifying lake and warehouse capabilities for multiple data types, and the SQL Endpoint is a surface to run SQL on data in the lake rather than the storage of a persistent relational store. A KQL database uses a different query language, not SQL, so it doesn’t meet the requirement.

This option fits because the Warehouse in Fabric is the dedicated relational compute layer designed for SQL workloads. It provides a true relational data store where you can create and manage tables with a relational schema and run standard SQL queries, all while storing the data in OneLake. This aligns precisely with the goal of a Fabric analytical data store that supports SQL against a relational schema and uses OneLake for storage.

The Lakehouse is broader, unifying lake and warehouse capabilities for multiple data types, and the SQL Endpoint is a surface to run SQL on data in the lake rather than the storage of a persistent relational store. A KQL database uses a different query language, not SQL, so it doesn’t meet the requirement.