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The KQL cheat sheet for Real-Time Intelligence
Two pages with the operators, patterns and performance habits I reach for every week in Eventhouse and Fabric projects. Keep it next to your query editor.
- Copy-paste examples that work
- Checked against the Microsoft documentation
- Includes update policies and materialized views
What is inside
Query basics
project, extend, where, summarize, top and distinct, with the filters that are actually fast.
Dates, strings and JSON
bin, datetime_diff, parse, extract, parse_json and mv-expand without the guesswork.
Joins and lookups
Which join kind to use, when lookup beats join, and the broadcast and shuffle hints.
Time series and anomalies
make-series, series_decompose_anomalies and the chart types you can render.
Real-Time Intelligence
Update policies, materialized views, retention and caching for Eventhouse.
Fast and cheap queries
A checklist of ten habits that cut query time and capacity use.
Made by someone who uses KQL in production
I am Brian Bønk, Microsoft Data Platform MVP, and I help teams build Real-Time Intelligence solutions in Microsoft Fabric. If your KQL is slow or expensive, or you are planning an Eventhouse solution, I am happy to take a look.