Improving throughput on real-time streaming components

Optimizing Eventstreams and Eventhouses in Microsoft Fabric focuses on reducing latency and cost by filtering/aggregating data as early as possible in the stream, and by tuning caching, partitioning, and update policies in the KQL database that backs an Eventhouse. Efficient configuration keeps capacity unit (CU) consumption low while preserving near-real-time query performance for downstream reporting and Real-Time Dashboards.

1 · Learn the must-know

• 1Apply Eventstream transformations (filter, aggregate, group by, managed field mapping) before data lands in an Eventhouse to cut ingestion volume and downstream compute cost.
• 2In an Eventhouse's KQL database, set the caching policy so only the hot/recent data needed for frequent queries stays in the fast SSD cache, while older data is offloaded to cheaper long-term storage.
• 3Use update policies to transform and reshape incoming data automatically as it arrives, avoiding costly post-ingestion ETL and duplicate storage of raw and transformed data.
• 4Use materialized views for frequently run aggregation queries so Fabric pre-computes and incrementally updates results instead of rescanning raw ingestion tables each time.
• 5Avoid over-partitioning or creating excessive small extents; align batching/ingestion settings so extents merge efficiently, since too many small shards degrade query performance.
• 6Monitor Eventstream and Eventhouse consumption via the Fabric Capacity Metrics app and built-in monitoring to spot throttling or high CU usage, then rescale capacity or streamline transformations/queries accordingly.

2 · Check your understanding