You plan to implement Fabric to store company sales data and support yearly ad-hoc analysis of legacy accounting data stored in Azure Data Lake Storage Gen2. Which architecture and integration approach minimizes administrative effort and costs?

You plan to implement Fabric to store company sales data and support yearly ad-hoc analysis of legacy accounting data stored in Azure Data Lake Storage Gen2. Which architecture and integration approach minimizes administrative effort and costs?

• A. Ingest the sales data into the Fabric lakehouse and set up a shortcut to the legacy accounting data in the storage account.
• B. Store the legacy data in a separate warehouse and query from BI tools.
• C. Move all data into a Fabric warehouse and refresh yearly.
• D. Create separate lakehouses for legacy and current data.

Answer: (A)

Using a Fabric lakehouse and linking to the existing Azure Data Lake Gen2 data with a data shortcut is the leanest way to meet the goal. Ingesting the current sales data into the Fabric lakehouse creates a single, governed analytics surface for daily or frequent analysis, while keeping the legacy accounting data in place and accessible via a shortcut. This lets you run yearly, ad-hoc analyses across both datasets without duplicating data or building and maintaining extra ETL pipelines. The shortcut approach minimizes administrative effort and cost because you avoid moving or duplicating large volumes of legacy data and you don’t need to manage separate data stores or schemas. You get the performance and governance of the Fabric lakehouse for the current data, and you can query the legacy data on demand as if it were part of the same environment. Other options introduce extra overhead: storing legacy data in a separate warehouse or moving all data into a Fabric warehouse requires ongoing ETL, synchronization, and maintenance; creating separate lakehouses for legacy and current data fragments governance and complicates access control and cost management.

Using a Fabric lakehouse and linking to the existing Azure Data Lake Gen2 data with a data shortcut is the leanest way to meet the goal. Ingesting the current sales data into the Fabric lakehouse creates a single, governed analytics surface for daily or frequent analysis, while keeping the legacy accounting data in place and accessible via a shortcut. This lets you run yearly, ad-hoc analyses across both datasets without duplicating data or building and maintaining extra ETL pipelines.

The shortcut approach minimizes administrative effort and cost because you avoid moving or duplicating large volumes of legacy data and you don’t need to manage separate data stores or schemas. You get the performance and governance of the Fabric lakehouse for the current data, and you can query the legacy data on demand as if it were part of the same environment.

Other options introduce extra overhead: storing legacy data in a separate warehouse or moving all data into a Fabric warehouse requires ongoing ETL, synchronization, and maintenance; creating separate lakehouses for legacy and current data fragments governance and complicates access control and cost management.