Most teams stop at Azure Monitor defaults and wonder why their alerting feels broken. Out-of-the-box dashboards. Static thresholds. An operations team that stopped trusting alerts six months ago because they cry wolf constantly.
This series provides production-tested KQL patterns for context-aware monitoring, correlation analysis, anomaly detection, and automated alerting strategies. The patterns come from years of tuning observability across managed services environments, where alert fatigue wasn’t theoretical. It was the thing that made customers leave.
The Core Problem
Azure Monitor gives you metrics collection, log aggregation, basic alerting, and pre-built dashboards. What it doesn’t give you: dashboards that match your business context, alerts that understand the difference between symptoms and root causes, correlation between data sources, or cost efficiency at scale.
If you’re getting worn out by events that aren’t problems while actual incidents slip through unnoticed, this is why.
What You’ll Learn
Part 1: The Reality of Enterprise Monitoring
Why defaults aren’t enough. Context-aware monitoring patterns that stop you from drowning in noise. Dynamic baselines that adapt to your environment’s actual behavior.
Part 2: Advanced KQL Patterns
Correlation analysis to see across systems. Anomaly detection to surface what’s genuinely unusual. Predictive patterns to catch problems before they arrive. The queries that build confidence you’re seeing the whole picture.
Part 3: Production-Ready Monitoring
Making patterns operational. Infrastructure-as-code alert deployment. Intelligent alerting strategies. Integration with the systems your team already uses. Monitoring that runs without you, escalates appropriately, and fixes what it can.
Why This Matters
In Platform Resiliency, the feedback loop between operations and platform teams is what turns incidents into improvements. That loop requires signal. Observability is how it sees.
Queries that run manually are just tools in a drawer. Queries that deploy automatically, suppress noise during maintenance, score business impact, and trigger remediation are operational infrastructure.
Who This Is For
Platform architects building observability infrastructure. Operations teams inheriting monitoring configurations. Engineers comfortable with KQL basics who want to go deeper. Anyone who’s inherited an environment where nobody knows why certain alerts exist.
The Throughline
This series provides the technical foundation for AI Observability. The patterns here extend to monitoring AI-enabled systems. The feedback loop concept connects directly to Platform Resiliency.
Start with one pattern that addresses your biggest pain point. Deploy it. Validate it. Expand from success.
