Question

A retail analytics team ingests clickstream data that arrives continuously from a web application into an Azure Event Hub. Business users need dashboards refreshed with a maximum data latency of a few seconds, and the incoming events must be filtered and enriched before landing in an Eventhouse for interactive KQL queries. The team wants a low-code approach that avoids managing Spark clusters. Which ingestion and transformation approach best fits these requirements?

Accepted Answer

Configure a Fabric Eventstream to consume the Event Hub, apply filter and manage-fields transformations in the stream, and route the output to an Eventhouse destination

Answer

Use a Data Factory pipeline with a Copy activity on a 5-minute schedule to load events into a Lakehouse, then query with the SQL analytics endpoint

Answer

Create a Dataflow Gen2 with incremental refresh scheduled every minute to pull events from the Event Hub into a Warehouse

Answer

Write a Spark Structured Streaming notebook that reads from the Event Hub, applies transformations, and writes to a Lakehouse Delta table