Training > Cloud & Containers > Getting Started with OpenTelemetry (LFS148)

Training Course

Learn to use OpenTelemetry to build and manage unified observability, skills increasingly important to IT developers and engineers career growth.

Who Is It For

This course is designed for software developers, DevOps engineers, site reliability engineers (SREs), and full-stack or backend developers looking to implement telemetry solutions across various applications and environments.

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What You’ll Learn

Understand the basics of OpenTelemetry, including how to instrument code for traces, metrics, and logs; and use manual and automatic instrumentation to enhance observability in modern applications.

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What It Prepares You For

You’ll be prepared to enhance application observability using OpenTelemetry, implement instrumentation, and operate the OpenTelemetry Collector, opening doors to roles requiring high-demand cloud and distributed system observability skills.

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Course Outline

Chapter 1. Course Introduction

Chapter 2. Why Do We Need OpenTelemetry?

Chapter 3. Overview of the OpenTelemetry Framework

Chapter 4. Hands-on Lab: OpenTelemetry in Action

Chapter 5. Instrumentation

Chapter 6. Hands-on Lab: Automatic Instrumentation and Instrumentation Libraries

Chapter 7. Hands-on Lab: Manual Instrumentation: Traces

Chapter 8. Hands-on Lab: Manual Instrumentation: Metrics

Chapter 9. Hands-on Lab: Manual Instrumentation: Logs

Chapter 10. OpenTelemetry Collector

Chapter 11. Hands-on Lab: Telemetry Pipelines with the OpenTelemetry Collector

Prerequisites

To make the best of this course, you will need to have the following:

Programming Knowledge: A basic understanding in programming, preferably with Python and Java
Basic Understanding of Distributed Systems: Knowledge of how distributed systems communicate and basic concepts of APIs
Experience with Observability Tools: While not strictly required, having some familiarity with existing observability tools like Prometheus, Grafana, or Jaeger can help understand the context and benefits of OpenTelemetry.
Command Line Interface (CLI) Skills: Ability to navigate and execute commands in a terminal or command prompt, as many setup and configuration tasks will involve CLI usage.
Environment Configuration: Experience with setting up and configuring development environments, including virtual environments and containerization technologies like Docker.
Version Control System (VCS) Usage: Familiarity with version control systems like Git, which is essential for managing code and collaborating on projects.

Lab Info

Recommended local setup: For a fully integrated development environment (IDE) with preconfigured linting, formatting, and language-specific settings, the following components are required:

Docker (or any compatible container runtime)
Visual Studio Code
Visual Studio Code Dev Containers extension

The provided Git repository includes a Dev Container configuration that sets up Python, Java, Docker, and the necessary environment variables. The flow of the hands-on lab has been designed and tested using this configuration.

This setup has been successfully tested on macOS, Windows, and Linux. It will require up to 4GB of RAM and 5GB of hard disk space.

Alternative cloud setup:

GitHub Codespaces also supports the Dev Container specification, allowing you to run the lab in a cloud environment. This requires a GitHub account and access to personal Codespaces plans.