The KQL Function Call Nobody Writes — KQL Queryset

KQL Queryset: Why Pipe-Forward Beats SQL for Time-Series

Episode 17 • 2026-04-24 Duration: 9:39

Matthias and Fabia explore the KQL Queryset in Microsoft Fabric — why the pipe-forward mental model beats SQL for time-series data, when to use make-series vs bin+summarize, and the architectural decision between KQL Queryset, Notebooks, and the SQL endpoint.

What we discuss

• A real-world mistake from a pre-Fabric era
• The one question that reframes the architectural debate
• How we got here — predecessor products and evolution
• Why the "obvious" answer is often wrong
• A real Reddit/Microsoft Q&A question unpacked
• The concrete recommended architecture
• F-SKU realism — what this actually costs
• When the rejected approach is actually right
• Risks of the recommended path
• What Microsoft is shipping that changes the calculus
• The architectural principle to take home

Key takeaways

• So — the lesson. Show me the query pattern. That's it. Don't pick your tool based on what you know. Pick it based on what the data needs. If you're doing time-series at scale, learn the pipe. It's worth it.
• I mean, fair question. If your workload is analytical reporting — quarterly trends, executive dashboards, scheduled refresh — Power BI connected through the SQL endpoint is probably the better path. You get a richer visualization library,...
• Right. And the naive answer is — just use the T-SQL endpoint, it supports SELECT statements. Which is true. But here's the thing. T-SQL on a KQL database is read-only DQL. SELECT only. No DDL, no management commands. And more importantly —...

Watch the episode

The Fabric Friday video on this topic: https://youtu.be/8amqWDeHT2Y

Resources

• Query data in a KQL queryset
• Create a KQL queryset
• Kusto Query Language overview
• SQL to KQL cheat sheet
• KQL quick reference
• make-series operator
• series_decompose_anomalies()
• Anomaly detection and forecasting
• Time series analysis
• render operator
• Share KQL queries
• Create a Real-Time Dashboard
• Real-Time Intelligence tutorial part 5: Query streaming data using KQL
• Tutorial: Learn common operators
• Tutorial: Use aggregation functions

About the show

Built on ElevenLabs voice synthesis. Matthias — cloned voice. Fabia — designed AI co-host. See Matthias live on YouTube (Fabric Friday), at his meetups, and at conferences like FabCon.

Hosted by Matthias Falland — Microsoft Data Platform MVP and community architect behind the Fabric Periodic Table. New episodes every Friday.

Submit your case

Have an architecture decision you are wrestling with? DM Matthias on LinkedIn — find him as Matthias Falland. Three to five sentences about the decision, your team size, and your current stack. We anonymize before airing.

Built on ElevenLabs voice synthesis. Brand design based on fabricperiodictable.com.