KQL Database: Why Time-Series Data Needs Its Own Engine

Episode 16 • 2026-04-17 Duration: 10:26

Matthias and Fabia explore why KQL Database exists alongside four other analytical stores in Microsoft Fabric. They unpack the Eventhouse-as-building mental model, the caching vs retention trap, and when you should — and shouldn't — choose KQL over SQL.

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

• If your data is time-series, logs, or telemetry — and your queries are always filtered by time — KQL Database isn't just an option.
• Fair. And honestly, if your team has strong Python skills and your latency tolerance is minutes, not milliseconds — Lakehouse plus notebooks is a legitimate path. You get the Spark ecosystem, ML libraries, broader tooling. I wouldn't fight...
• Right. And that matters for the reversal. Because the naive answer teams land on is: just put your IoT data in the Lakehouse. Delta Lake handles everything, right?

Resources

• What is Real-Time Intelligence?
• Choose an analytical data store in Microsoft Fabric
• Eventhouse overview
• Data connectors overview
• Get data overview
• Change data policies
• KQL overview - scalar data types
• Eventhouse and KQL Database consumption
• Pricing cost drivers
• Create a KQL database
• Time series analysis
• Anomaly detection and forecasting
• Manage and monitor a database
• Manage and monitor an eventhouse
• KQL Database git integration

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.