Question

Your company runs a transactional workload on an Azure SQL Database that supports an e-commerce site. The analytics team wants to run large aggregate and reporting queries against near-real-time operational data without adding load to the production OLTP database or writing custom ETL pipelines. Management wants the data to land in Microsoft Fabric OneLake so it can be joined with other datasets using T-SQL and Spark. Which approach best meets these requirements with the least operational overhead?

Accepted Answer

Configure Fabric Mirroring for the Azure SQL Database so changes are continuously replicated into OneLake as Delta tables for analytics.

Answer

Enable a read scale-out replica on the Azure SQL Database and point the analytics team's reporting tools at the read-only endpoint.

Answer

Schedule an Azure Data Factory pipeline to copy the tables into a Fabric Warehouse every hour.

Answer

Create a geo-replica of the Azure SQL Database in a paired region and run the analytics queries against the secondary.