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How KQL Processes Data

3-4 hours · Module 1 · Free

Before you can write advanced KQL, you need to understand how KQL processes data at a fundamental level. This module breaks down the tabular data model, the operator pipeline that transforms data left to right, every data type you will encounter in security logs, type conversion patterns that prevent silent failures, and the null handling pitfalls that cause queries to silently drop results.

Every subsequent module builds on these foundations. If you understand how data flows through the pipeline, you can debug any query. If you do not, advanced operators become unpredictable black boxes.

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You've learned how KQL processes data.

K0 gave you the query language's place in the Microsoft security stack. K1 took you through the semantics — tables, operators, the pipe model, and why KQL isn't SQL. Now you write the queries that find what attackers hope you miss.

  • 12 modules of query craft — filtering and shaping, joins and unions, time-series analysis, summarisation, string manipulation, and geospatial analysis
  • 68 KQL exercises — every one with a realistic dataset, a reference solution, and a discussion of alternative approaches
  • K11 — Threat Hunting with KQL — the course's flagship module. Hypothesis-driven methodology, MITRE ATT&CK-aligned hunting across 7 techniques, UEBA composite risk scoring, and retroactive IOC sweeps
  • K13 Capstone — The Hunting Lab — three complete investigation scenarios requiring every query skill from the course
  • Hunt management and ROI metrics — the operating model that justifies KQL hunt programs to leadership
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