The Jaeger Course is designed to provide participants with the necessary skills to use Jaeger, an open-source distributed tracing system, to monitor and troubleshoot microservices architectures. Participants will learn how to integrate Jaeger with their applications, visualize traces to understand system performance, and optimize application behavior through trace analysis. By the end of the course, participants will be able to implement Jaeger for distributed tracing, enabling them to enhance their observability practices.

High-Level Training Flow – Jaeger Course

1. Training Needs Analysis (TNA)
   Assess participants' understanding of distributed systems, microservices, and observability practices, as well as their familiarity with other monitoring tools. This helps identify gaps in knowledge and ensures the course content is appropriate for their experience level.

2. Curriculum Finalization & Agenda Approval
   Finalize course modules, session schedules, and learning outcomes. Topics typically include:

   • Introduction to Jaeger and distributed tracing

   • Setting up Jaeger for distributed tracing

   • Instrumenting applications for trace collection

   • Understanding trace visualization in Jaeger

   • Advanced trace analysis for system performance

   • Integrating Jaeger with existing observability stacks (e.g., Prometheus, Grafana)
     The agenda is reviewed and approved to ensure it aligns with participants' goals.

3. Environment Setup
   Prepare the necessary tools and infrastructure, including:

   • Installing and configuring Jaeger (e.g., using Docker or Kubernetes)

   • Setting up trace collection for applications (e.g., Java, Go, Node.js)

   • Ensuring access to cloud environments or local machines where Jaeger can be deployed and tested

4. Content Preparation
   Develop slides, demos, hands-on exercises, and real-world use cases. Content will cover:

   • Jaeger's architecture and components (agent, collector, storage)

   • Integrating Jaeger with applications and services for tracing

   • Visualizing and analyzing traces in the Jaeger UI

   • Troubleshooting performance issues using trace data

   • Best practices for distributed tracing in microservices environments

5. Training Delivery
   Conduct live sessions combining theoretical learning with hands-on labs. Participants will learn how to:

   • Install and configure Jaeger in a microservices environment

   • Integrate Jaeger with different programming languages and frameworks (e.g., Java, Go, Python)

   • Collect traces from various services and visualize them in Jaeger's UI

   • Analyze distributed traces to troubleshoot latency, errors, and bottlenecks

   • Set up alerts and monitoring based on trace data

6. Daily Recap & Lab Review
   Summarize key points at the end of each session, review hands-on exercises, and answer participant questions. This ensures understanding and retention of trace collection, visualization, and analysis techniques.

7. Assessment & Project Submission
   Evaluate participants through quizzes, hands-on exercises, and a final project. The project typically involves setting up Jaeger for an existing application, collecting distributed traces, and identifying potential system performance issues.

8. Feedback Collection
   Gather feedback on course content, pacing, hands-on exercises, and overall relevance. This feedback helps adjust the course structure for future iterations and ensures that participants have learned what they need to apply Jaeger effectively.

9. Post-Training Support
   Provide ongoing support via Q&A sessions, Slack/Telegram groups, or email for troubleshooting, integrating Jaeger with other observability tools, or applying learned concepts in real-world environments.

10. Training Report Submission
    Document attendance, assessment results, project completion, and participant feedback. The final training report outlines participant progress, their ability to apply Jaeger to real-world tracing use cases, and readiness to integrate Jaeger into their applications.