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LinkedIn Learning

Mastering Observability with OpenTelemetry

via LinkedIn Learning

Overview

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Learn how to leverage OpenTelemetry for actionable insights into the performance of your applications and services.

Syllabus

Introduction
  • OpenTelemetry unveiled: Mastering modern observability
  • What you should know
  • Setting up your development environment
  • Using the exercise files
  • Getting to know the sample app
  • Setting up the sandbox
1. Introduction to OpenTelemetry
  • A brief history of monitoring and observability
  • Understanding distributed tracing
  • What is (auto) instrumentation?
  • What is OpenTelemetry?
  • Understand OpenTelemetry's architecture
  • Important resources
2. Diving Into Tracing with OpenTelemetry
  • Setting up the provided Node tracing module
  • Instrumenting Node.js for tracing
  • Instrumenting Python for tracing
  • Introducing Jaeger
  • Running Jaeger in Docker
  • Exporting spans to Jaeger
  • Analyzing traces with Jaeger
  • Triage and manual instrumentation
  • Finding the root cause in Jaeger
3. Using the OpenTelemetry Collector
  • Introduction to the OpenTelemetry Collector
  • A primer on sampling
  • The goal for using the OpenTelemetry Collector
  • Installing a local Collector
  • Collector configuration
  • Debugging and zPages
4. Observability with Grafana
  • Why Grafana?
  • The goal for observability with Grafana
  • Creating a Grafana Cloud account
  • Setting up the Grafana Collector integration
  • Analyzing trace data in Grafana
5. Collecting Metrics
  • Metrics in OpenTelemetry
  • Adding metrics instrumentation to your code
  • Viewing metrics in Grafana
6. Collecting Logs
  • Logging in OpenTelemetry
  • Configuring Python for logging
  • Viewing logs in Grafana
Conclusion
  • Next steps

Taught by

Daniel Khan

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4.9 rating at LinkedIn Learning based on 22 ratings

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