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Introduction to Tempo for Distributed Tracing

Ce cours n'est pas disponible en Français (France)

Nous sommes actuellement en train de le traduire dans plus de langues.

Introduction to Tempo for Distributed Tracing

Instructeur : Hurix Digital

Inclus avec

3 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

4 heures à compléter

Planning flexible

Apprenez à votre propre rythme

3 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

4 heures à compléter

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

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.

Compétences que vous acquerrez

Catégorie : Microservices
Catégorie : Multi-Tenant Cloud Environments
Catégorie : Software Visualization
Catégorie : Incident Response
Catégorie : Dependency Analysis
Catégorie : Incident Management
Catégorie : Application Performance Management
Catégorie : Data Pipelines
Catégorie : Performance Tuning

Outils que vous découvrirez

Catégorie : Kubernetes
Catégorie : Grafana

Détails à connaître

Certificat partageable

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avril 2026

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Il y a 3 modules dans ce cours

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.

Détails du module

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

Inclus

1 vidéo1 lecture2 devoirs

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

Inclus

2 vidéos3 lectures1 devoir

2 vidéosTotal 11 minutes

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

3 lecturesTotal 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