Question

A retail company streams point-of-sale transactions into a Fabric Eventstream. Analysts need to run sub-second KQL queries against the incoming data with full indexing and the lowest possible query latency, while also making the same data available in the lakehouse for periodic Spark batch reporting without duplicating storage. Which approach should you implement for the Eventhouse destination?

Accepted Answer

Land the data in a native Eventhouse (KQL) table for low-latency queries, then create a OneLake shortcut from the lakehouse to that table for Spark batch reporting

Answer

Create a OneLake shortcut in the Eventhouse pointing to a lakehouse Delta table, and query the shortcut with KQL

Answer

Write the Eventstream output only to a lakehouse Delta table and query it directly with KQL from the Eventhouse

Answer

Copy the data into both a native Eventhouse table and a separate lakehouse Delta table using a scheduled pipeline