Monitoring and Observability
Monitoring and observability are about understanding what is happening inside your running systems. Monitoring tells you when something is wrong by tracking known failure modes through dashboards and alerts. Observability goes further -- it gives you the ability to ask new questions about system behavior without deploying new code, by providing rich, structured telemetry data that can be explored and correlated.
As systems grow more distributed, the ability to trace a single request across dozens of services, correlate logs with metrics, and detect anomalies before they become outages becomes critical. Investing in observability is not optional at scale -- it is how teams debug production issues, validate deployments, and maintain reliability over time.
What You'll Learn
- Three Pillars (Metrics, Logs, Traces) - The foundational data types of observability, how they complement each other, and how to use them together to build a complete picture of system health.
- Metrics & Alerting - Collecting and aggregating time-series metrics, defining SLIs/SLOs/SLAs, building effective dashboards, and designing alert rules that minimize noise and maximize signal.
- Distributed Tracing - Tracking requests as they flow across service boundaries using trace context propagation, span trees, and tools like Jaeger and OpenTelemetry.
- Logging at Scale - Structured logging, centralized log aggregation, log levels and sampling strategies, and managing storage costs when systems produce millions of log lines per minute.
Prerequisites
Understanding of distributed systems concepts, networking basics, and microservice architectures will provide helpful context. Familiarity with basic statistics (percentiles, averages) is useful for working with metrics and alerting thresholds.