OpenTelemetry for Observability: The Complete Course

<div>Welcome to OpenTelemetry for Observability: The Complete Course! Are you ready to take full control of your distributed systems and build the production-grade observability platform your applications deserve? This course is designed to take you from observability fundamentals to a fully instrumented, Kubernetes-deployed system - using OpenTelemetry, Prometheus, Loki, Tempo, and Grafana.</div><div><br></div><div>Why Learn OpenTelemetry and Observability?</div><div><br></div><div>Modern software systems are distributed, dynamic, and complex. When something breaks in production, you need answers fast — and the difference between a five-minute fix and a five-hour outage often comes down to how well your system is instrumented. Here is why mastering OpenTelemetry and observability is essential right now:</div><div><ul><li>OpenTelemetry Is the Industry Standard for Telemetry: OpenTelemetry is a CNCF-graduated project and the vendor-neutral standard for generating and collecting observability data. It is rapidly replacing proprietary instrumentation SDKs across the industry. Learning OpenTelemetry means your instrumentation code is portable across any backend - Prometheus, Grafana Cloud, Datadog, Honeycomb, and beyond - without rewriting a single line.</li><li><span style="font-size: 1rem;">Metrics, Logs, and Traces Are No Longer Optional: Observability is not a nice-to-have anymore. Engineering organizations operating distributed systems rely on correlated metrics, logs, and traces to detect issues, diagnose root causes, and validate reliability targets. Teams that instrument their systems correctly ship with confidence.</span></li><li><span style="font-size: 1rem;">Prometheus and Grafana Are the Backbone of Cloud-Native Observability: Prometheus is the de facto standard for metrics collection in Kubernetes environments. Grafana is the leading visualization and exploration platform across all three signal types. Together with Loki for logs and Tempo for traces, they form a complete open-source observability stack that is production-proven at scale (and entirely free to use!).</span></li><li><span style="font-size: 1rem;">Distributed Tracing Solves the Problems Metrics Alone Cannot: When a request fails or slows down in a system with multiple services, metrics tell you something is wrong, but traces tell you exactly where and why. OpenTelemetry distributed tracing gives you end-to-end visibility across service boundaries, including asynchronous message queue patterns where traditional tracing tools fall short.</span></li><li><span style="font-size: 1rem;">High Market Demand for Observability Skills: As organizations shift to microservices, platform engineering, and SRE practices, the demand for engineers who understand SLIs, SLOs, error budgets, and modern instrumentation is accelerating. Observability expertise consistently differentiates candidates for senior DevOps, platform, and SRE roles.</span></li></ul><span style="font-size: 1rem;">By investing time in this course, you are building one of the most practical and transferable skill sets in modern software engineering: skills that apply regardless of the language, framework, or cloud provider your team uses.</span></div><div><br></div><div>Why Should You Choose This Course?</div><div><br></div><div>This course goes far beyond a surface-level introduction to OpenTelemetry. You will build and instrument a real distributed application end-to-end, using the same tools and workflows used in production environments today.</div><div><ul><li>Learn by Doing with Extensive Hands-On Labs: Every concept in this course is immediately followed by a practical lab. You will instrument real code, deploy real infrastructure, and debug real issues. I provide the task, give you space to try it yourself, and then walk through the solution step by step.</li><li><span style="font-size: 1rem;">Instrument a Real Distributed Application: We do not instrument a toy "Hello World" app. The course target is a distributed translation application with a Node.js frontend, a Python background worker, and a Redis queue. This multi-language, asynchronous architecture represents the kind of real-world complexity where observability truly matters.</span></li><li><span style="font-size: 1rem;">Complete Coverage of All Three Observability Signals: Most courses focus on one signal type. This course covers metrics, logs, and traces in equal depth, including both automatic and manual instrumentation for each, applied to both services in the system.</span></li><li><span style="font-size: 1rem;">Distributed Context Propagation Across Async Boundaries: One of the most challenging and most valuable skills in observability engineering is connecting traces across services that communicate asynchronously. This course tackles that challenge directly, walking you through bidirectional context propagation across a message queue so that a single end-to-end trace spans the entire system.</span></li><li><span style="font-size: 1rem;">AI-Assisted Workflows Integrated Throughout: From auditing Kubernetes manifests to diagnosing deployment bugs, the course integrates AI tooling in a practical and realistic way. You will see how to use AI assistants to accelerate instrumentation, improve manifest quality, and speed up root cause analysis.</span></li><li><span style="font-size: 1rem;">Kubernetes Deployment Included: The course does not stop at local development. You will migrate the full application and observability stack to Kubernetes using Kustomize, deploy to a live cluster, and verify that all three observability signals are flowing correctly in a real production-like environment.</span></li></ul></div><div><br></div><div>Which Skills Will You Acquire During This Course?</div><div><br></div><div>As you progress through the lectures and labs, you will gain a comprehensive set of observability and instrumentation skills, including:</div><div><ul><li>Building an Observability Stack from Scratch: You will deploy and connect Prometheus, Loki, Tempo, and Grafana, configure the OpenTelemetry Collector as a centralized telemetry pipeline, and verify that the full stack is operational before writing a single line of instrumentation code.</li><li><span style="font-size: 1rem;">Defining Reliability Targets with SLIs, SLOs, and SLAs: You will learn to define user-centric Service Level Indicators, set Service Level Objectives as internal reliability targets, understand the difference between SLOs and contractual SLAs, and use error budgets to make informed decisions about deployment velocity versus stability.</span></li><li><span style="font-size: 1rem;">Applying OpenTelemetry Automatic Instrumentation: You will enable automatic instrumentation for Node.js and Python services to capture framework-level metrics, logs, and traces with minimal code changes, giving you broad observability coverage as a starting point for both services.</span></li><li><span style="font-size: 1rem;">Creating Custom Metrics with Counters and Histograms: You will implement manual metrics instrumentation to capture domain-specific business events that automatic instrumentation cannot surface. You will add dimensional labels to your metrics and explore them in Prometheus using PromQL.</span></li><li><span style="font-size: 1rem;">Building Custom Spans for Business Logic Tracing: You will create manual OpenTelemetry spans to trace business-level operations inside your services, set span attributes and error status, and manage span lifecycle correctly to produce accurate, informative traces visible in Grafana Tempo.</span></li><li><span style="font-size: 1rem;">Configuring Structured Logging with Trace Context Injection: You will replace unstructured log output with structured logging integrated with the OpenTelemetry SDK so that every log entry automatically carries trace context. You will then configure log-trace correlation in Grafana for one-click navigation between a log entry and its corresponding trace.</span></li><li><span style="font-size: 1rem;">Implementing Distributed Context Propagation: You will manually inject and extract trace context across an asynchronous message queue boundary to connect frontend and worker spans into a single end-to-end trace. You will implement the full round trip and verify the complete trace in Grafana Tempo.</span></li><li><span style="font-size: 1rem;">Using the Exporter Pattern for Third-Party Services: You will deploy a Redis exporter as a sidecar to collect operational metrics from Redis, which has no native OpenTelemetry support, and expose those metrics to Prometheus. This pattern applies to any third-party service in your stack.</span></li><li><span style="font-size: 1rem;">Deploying to Kubernetes with Kustomize: You will write Kubernetes manifests for the full application and observability stack, manage multi-namespace configuration with Kustomize, deploy to a local cluster, diagnose and fix telemetry collection bugs, and verify end-to-end signal flow through a live Kubernetes environment.</span></li></ul></div><div><span style="font-size: 1rem;">Get ready to build the observability platform your distributed systems deserve. Whether you are an engineer who has never written a span in your life or a practitioner looking to master OpenTelemetry properly, this course will give you the depth, the practice, and the confidence to instrument any system, in any language, on any platform. Let's get started!</span></div>

What you'll learn:

• Define SLIs, SLOs, and SLAs and use error budgets to balance reliability with development velocity
• Deploy a local observability stack with Prometheus, Loki, Tempo, and Grafana
• Instrument NodeJS and Python applications with all three observability signals
• Implement distributed context propagation across asynchronous service boundaries
• Apply automatic instrumentation to capture framework-level metrics, logs, and traces with minimal code
• Create custom metrics using counters and histograms to track business-level events
• Build custom spans with attributes and error handling for precise trace visibility into business logic
• Configure structured logging with automatic trace context injection for consistent cross-signal correlation
• Configure the OpenTelemetry Collector as a vendor-agnostic telemetry pipeline
• Use the exporter pattern to collect metrics from third-party services without native OpenTelemetry support
• Write Kubernetes manifests for both application services and the full observability stack
• Manage multi-namespace Kubernetes deployments with Kustomize for consistent, reproducible releases
• Verify end-to-end telemetry data flowing correctly through a live Kubernetes cluster