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OpenTelemetry for Observability: The Complete Course

Learn to instrument real applications with metrics, logs, and traces using OpenTelemetry, build a complete observability stack with Prometheus, Loki, Tempo, and Grafana, and deploy everything to Kubernetes with confidence.
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115 Lectures

Comprehensive Knowledge

10 Hours

Video Duration

30+ Labs

Focus on Practice

Course Certificate

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What you are going to learn

From Zero Visibility to Full Observability: Across Every Service, Every Signal, Every Environment

This course takes you from the fundamentals of observability to instrumenting a real distributed application with all three signal types (metrics, logs, and traces) using OpenTelemetry. You will deploy a complete local observability stack, configure the OpenTelemetry Collector as a centralized telemetry pipeline, and instrument both a Node.js frontend and a Python worker service using automatic and manual instrumentation. You will then connect their traces across an asynchronous message queue boundary through distributed context propagation, giving you a single end-to-end trace that spans both services.

By the end of this course, you will be able to implement a production-grade observability strategy from the ground up, correlate signals across services in Grafana to diagnose real issues, and deploy your fully instrumented application and observability stack to Kubernetes using Kustomize. You will leave with hands-on experience across the full observability lifecycle: from writing your first custom span to verifying end-to-end telemetry in a live cluster.

By completing this course, you will be able to:

  • Define SLIs, SLOs, and SLAs and use error budgets to balance reliability with development velocity
  • Instrument NodeJS and Python applications with all three observability signals
  • Deploy a local observability stack with Prometheus, Loki, Tempo, and Grafana
  • Implement distributed context propagation across asynchronous service boundaries
  • 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
  • Apply automatic instrumentation to capture framework-level metrics, logs, and traces with minimal code
  • Write Kubernetes manifests for both application services and the full observability stack
  • Create custom metrics using counters and histograms to track business-level events
  • Manage multi-namespace Kubernetes deployments with Kustomize for consistent, reproducible releases
  • Build custom spans with attributes and error handling for precise trace visibility into business logic
  • Verify end-to-end telemetry data flowing correctly through a live Kubernetes cluster
  • Configure structured logging with automatic trace context injection for consistent cross-signal correlation

Course Contents

Frequently asked questions

Who is this course designed for?

This course is designed for multiple technical roles seeking to deepen their Python and DevOps expertise.
  • Software Engineers and Developers who want to add production-grade observability to their applications will learn how to instrument code in both Node.js and Python with OpenTelemetry and understand which signals to use for different scenarios.
  • DevOps and Platform Engineers responsible for operating distributed systems will gain hands-on experience setting up a complete observability stack, configuring the OpenTelemetry Collector, and deploying everything to Kubernetes with proper configuration management.
  • Site Reliability Engineers looking to improve incident response and reduce mean time to resolution will learn how to correlate metrics, logs, and traces in Grafana to diagnose issues faster and with greater confidence.
  • Tech Leads and Architects evaluating observability strategies will gain a thorough understanding of OpenTelemetry's vendor-neutral approach and how to build an observability platform that is not locked into any single tool or vendor.

What prior knowledge do I need before taking this course?

You should have a working knowledge of at least one programming language and be comfortable reading application code in Node.js and Python. Familiarity with running commands in a terminal and using Docker is important, as the course labs make extensive use of both.

Basic knowledge of Kubernetes is helpful for the final section, where the full stack is deployed to a cluster. No prior experience with OpenTelemetry, observability tooling, or instrumentation of any kind is required — all concepts are introduced from the ground up.

Will I incur costs (cloud provider/tools) while taking this course?

The course can be completed entirely for free using local tooling. Docker Desktop is free for personal use, and all observability tools covered — OpenTelemetry, Prometheus, Loki, Tempo, Grafana, and Kustomize — are open source and free to use.

The Kubernetes section uses a local cluster created with Kind or Minikube, which requires no cloud account and incurs no cost. No paid services or cloud provider accounts are required at any point in the course.

Does the course cover a specific programming language?

The instrumentation labs use two languages: Node.js for the frontend service and Python for the background worker service. This pairing is intentional — it demonstrates how OpenTelemetry provides a consistent instrumentation model across different language ecosystems, which is one of its core strengths.

The observability concepts, Collector configuration, Grafana usage, and Kubernetes deployment sections are language-agnostic and apply directly to any technology stack.

Is this course tied to a specific observability vendor or platform?

No. OpenTelemetry is a vendor-neutral, CNCF-graduated project, and this course reflects that philosophy throughout. The instrumentation code you write is not tied to any backend — you could swap Prometheus for another metrics store or Loki for a different log aggregator without changing a single line of application code.

The backends used in the course (Prometheus, Loki, Tempo, and Grafana) are all open source and were chosen because they are widely adopted and freely available. The skills you build transfer directly to commercial observability platforms such as Datadog, Grafana Cloud, Honeycomb, or any other vendor that supports the OpenTelemetry protocol.