Modern software doesn't run on one server anymore - it runs across dozens of microservices, containers, and cloud regions, and when something breaks, "check the logs" is no longer good enough.OpenTelemetry & Modern Observability is a hands-on, engineering-first guide to building real visibility into distributed systems using OpenTelemetry - the open, vendor-neutral standard now powering observability at organizations of every size. Written by a practicing engineer, this book skips the marketing language and goes straight to what actually works in production: instrumenting real applications, correlating traces, metrics, and logs into one investigation, deploying and scaling the OpenTelemetry Collector, and running all of it reliably on Kubernetes.Across 14 chapters and a full production-grade capstone project, you'll learn how to: - Instrument HTTP services, databases, external APIs, and background jobs with runnable, copy-paste-ready code- Build and read distributed traces to find the real root cause of latency and failures- Design metrics that scale - and avoid the cardinality mistakes that blow up observability bills- Correlate logs, traces, and metrics so every investigation starts with evidence, not guesswork- Deploy, scale, and secure the OpenTelemetry Collector, including tail sampling and PII redaction- Bring it all together on Kubernetes with the OpenTelemetry Operator, DaemonSets, and gateway architectures- Connect telemetry to real reliability engineering: SLOs, error budgets, and incident response- Build a complete, production-grade observability platform from scratch - then break it on purpose and diagnose the failures using nothing but the telemetry it producesEvery chapter includes fully runnable code (Python, YAML, PromQL), realistic production scenarios, and the hard-won lessons that only come from operating these systems under real incident pressure. Whether you're a backend engineer, SRE, DevOps engineer, or platform engineer, this book will teach you not just how to use OpenTelemetry, but how to think like an observability engineer.
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