Introduction to Tempo for Distributed Tracing

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Introduction to Tempo for Distributed Tracing

Instructor: Hurix Digital

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3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Multi-tenant setup with Tempo distributors enables scalable tracing with strong data isolation across teams and apps.
Waterfall trace analysis highlights critical paths, helping quickly spot latency issues and bottlenecks.
Correlating traces with logs and metrics speeds up root cause analysis and validates fixes effectively.
Sampling, retention, and trace correlation ensure scalable, sustainable observability as systems grow.

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There are 3 modules in this course

Distributed tracing has become essential for troubleshooting microservices at scale – 73% of organizations struggle with service dependencies visibility. This Short Course was created to help IT Support and Operations professionals accomplish effective microservices troubleshooting through Grafana Tempo distributed tracing. By completing this course, you'll be able to configure production-ready trace ingestion pipelines, identify performance bottlenecks through trace analysis, and correlate traces with logs for rapid incident resolution.

By the end of this course, you will be able to: • Apply Tempo ingestion settings to route OpenTelemetry traces from Kubernetes services into a multi-tenant storage backend • Analyze trace visualizations to pinpoint inter-service latency hotspots and identify the upstream service causing a performance regression • Evaluate trace-to-logs correlations to confirm root cause and validate remediation effectiveness during post-incident review This course is unique because you'll work with real Kubernetes environments and enterprise-grade configurations using Tempo's multi-tenant architecture. To be successful in this project, you should have background experience with Kubernetes operations and basic observability concepts.

Configure Tempo ingestion settings to route OpenTelemetry traces from Kubernetes services into multi-tenant storage backends.

What's included

1 video1 reading2 assignments

Analyze trace visualizations to pinpoint inter-service latency hotspots and identify upstream service causing performance regressions.

What's included

2 videos3 readings1 assignment

2 videosTotal 11 minutes

The Cost of Hidden Performance Issues3 minutes
Understanding Trace Waterfall Visualizations and Span Analysis8 minutes

3 readingsTotal 30 minutes

Service Dependency Maps and Latency Pattern Recognition10 minutes
Systematic Trace Analysis Methodology with Grafana Visualization Guide15 minutes
Hands-On Trace Analysis Practice5 minutes

1 assignmentTotal 5 minutes

Knowledge Check: Trace Visualization Analysis5 minutes

Evaluate trace-to-logs correlations to confirm root cause and validate remediation effectiveness during post-incident review.

What's included

3 readings3 assignments

3 readingsTotal 30 minutes

Post-Incident Review: When Traces Meet Logs3 minutes
Correlation Techniques and Root Cause Validation Strategies12 minutes
Correlation Evaluation Framework and Validation Techniques15 minutes

3 assignmentsTotal 65 minutes

Post-Incident Correlation Analysis Report5 minutes
Knowledge Check: Trace-to-Logs Correlation Evaluation30 minutes
Course Assessment: Distributed Tracing Operations Mastery30 minutes

Instructor

Hurix Digital

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