Question

A retail analytics team ingests clickstream events into an Eventhouse via an Eventstream. Raw JSON events land in a table named RawEvents. The team needs every incoming row to be automatically parsed, filtered to remove bot traffic, and written to a curated table with a flattened schema — with the transformation applied at ingestion time inside the KQL database, requiring no external orchestration and minimal added latency. Which approach should they use?

Accepted Answer

Create a KQL update policy on the curated table that runs a transformation query against RawEvents at ingestion time

Answer

Schedule a Dataflow Gen2 to read RawEvents every 5 minutes, transform, and load the curated table

Answer

Add a Spark Structured Streaming job that reads the Eventhouse table and writes the curated output

Answer

Create a KQL materialized view over RawEvents to deduplicate and flatten the data