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    Log Analytics

    Azure Log Analytics & KQL

    Practical guides to Azure Log Analytics, KQL queries, and using logs for more than just troubleshooting.

    Beyond Basic Logging

    Most people use Log Analytics for troubleshooting. But it’s capable of so much more: performance monitoring, cost optimization, security analysis, and operational intelligence.

    These posts show you how to actually use Log Analytics as a log aggregation tool, not just a performance monitor.

    What you’ll learn:

    • KQL query patterns that scale
    • Log aggregation strategies
    • Performance monitoring
    • Creating human-readable reports
    • Query optimization
    • Real-world use cases

    Log Analytics Posts

    Why Log Analytics Matters

    In cloud environments, logs are your primary source of truth for:

    • Troubleshooting - Understanding what went wrong
    • Performance - Identifying bottlenecks
    • Security - Detecting threats and anomalies
    • Cost - Tracking resource usage
    • Compliance - Audit trails and reporting

    But only if you know how to query them effectively.

    KQL Tips

    Kusto Query Language (KQL) is powerful but has a learning curve. Key patterns I use:

    • Start with time ranges to limit data
    • Use where before extend for performance
    • Avoid parse_json in alerts (resource intensive)
    • Build reusable functions for common queries
    • Test in the Log Analytics Demo

    Have KQL questions or want to share a query pattern? Leave a comment on any post.