With 1 TB legacy accounting data in Azure Data Lake Gen2 and annual ad-hoc reporting needs, which Fabric architecture and integration approach minimizes admin costs while enabling reporting and analysis?

• A. Ingest the sales data into the Fabric warehouse and use a pipeline to move the legacy accounting data into the warehouse
• B. Ingest the sales data into the Fabric lakehouse and set up a shortcut to the legacy accounting data in the storage account
• C. Ingest everything into a single lakehouse with no shortcuts
• D. Use a data mart for storage with daily ETL

Answer: (B)

The idea being tested is how to enable reporting across both current and legacy data with minimal ongoing admin work. In Fabric, the lakehouse is the centralized analytics workspace, and you can reference data that lives outside the lakehouse by using shortcuts. This lets you query and analyze together without physically duplicating data or building new pipelines for the legacy store.

In this scenario, bringing only the new sales data into the lakehouse and creating a shortcut to the 1 TB of legacy accounting data in the storage account keeps the legacy data where it already exists. You get a unified analytics surface for reporting and ad-hoc analysis, while avoiding costly data movement, duplication, and the maintenance overhead of ETL pipelines to ingest the legacy data. The shortcut acts like a pointer that lets you join or compare the lakehouse data with the external data at query time, which minimizes admin tasks and storage costs.

Other approaches require moving or duplicating the legacy data into the warehouse or lakehouse, or setting up ongoing daily ETL to a data mart, all of which increases admin effort, storage, and governance overhead.