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

KQL Queryset: Why Pipe-Forward Beats SQL for Time-SeriesEpisode 17 • 2026-04-24 Duration: 9:39Matthias 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 discussA real-world mistake from a pre-Fabric eraThe one question that reframes the architectural debateHow we got here — predecessor products and evolutionWhy the "obvious" answer is often wrongA real Reddit/Microsoft Q&A question unpackedThe concrete recommended architectureF-SKU realism — what this actually costsWhen the rejected approach is actually rightRisks of the recommended pathWhat Microsoft is shipping that changes the calculusThe architectural principle to take homeKey takeawaysSo — 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 —...ResourcesQuery data in a KQL querysetCreate a KQL querysetKusto Query Language overviewSQL to KQL cheat sheetKQL quick referencemake-series operatorseries_decompose_anomalies()Anomaly detection and forecastingTime series analysisrender operatorShare KQL queriesCreate a Real-Time DashboardReal-Time Intelligence tutorial part 5: Query streaming data using KQLTutorial: Learn common operatorsTutorial: Use aggregation functionsAbout the showBuilt 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 caseHave 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.