Question

A retail company ingests point-of-sale transactions into a Fabric Eventstream from an Azure Event Hubs source. The data engineering team must route the events so that transactions with an amount greater than 5000 are sent to a KQL database for real-time fraud analysis, while all transactions (regardless of amount) are landed into a lakehouse table for batch reporting. They want to accomplish this entirely within the Eventstream without writing Spark structured streaming code. What is the most appropriate way to implement this?

Accepted Answer

Add a Filter event processor to create a derived stream for high-value transactions routed to the KQL database (eventhouse) destination, and add a separate destination from the source stream to the lakehouse table.

Answer

Configure a single lakehouse destination and use a downstream Dataflow Gen2 to filter and copy high-value rows into the KQL database on a schedule.

Answer

Use a Spark structured streaming notebook to read from Event Hubs, apply the amount filter, and write to both the KQL database and lakehouse in foreachBatch.

Answer

Add an Aggregate event processor to group transactions by amount and route the aggregated output to both the KQL database and lakehouse destinations.