Abstract

Developing workloads for deployment to Kubernetes comes with unique challenges. These include managing resource requirements and allocations, ensuring node capabilities match workload demands, and maintaining the stability and performance of microservices and applications.

This course will present hands-on scenarios in which you will use Datadog to identify, fix, and monitor common Kubernetes workload issues.

Learning Objectives

  • Use Kubernetes views and dashboards, Service Catalog, Log Management, and APM to troubleshoot and remedy Kubernetes related application issues
  • Isolate the root cause of issues common to applications running on Kubernetes
  • Select the best tools and features in Datadog to accelerate your investigation
  • Use Datadog to confirm that issues are resolved
  • Create monitors to alert you if issues recur

Primary Audience

  • SREs who are responsible for the performance and reliability of services running Kubernetes clusters
  • Software engineers who want to write and deploy fast, efficient, and dependable applications to Kubernetes clusters

Prerequisites

Technical Requirements

In order to complete the course, you will need:

  • Google Chrome or Firefox

Course Navigation

At the bottom of each lesson, click MARK LESSON COMPLETE AND CONTINUE button so that you are marked complete for each lesson and can receive the certificate at the end of the course.

Course Enrollment Period

Please note that your enrollment in this course ends after 30 days. You can re-enroll at any time and pick up where you left off.

Course curriculum

    1. Introduction

    1. Tools

    2. Strategy

    3. About the lab

    4. Lab: Troubleshooting Workloads

    1. Summary

    2. Feedback Survey

Monitoring a Kubernetes Cluster: Troubleshooting Workloads

  • 1.5 hours to complete
  • 2 Lessons
  • Intermediate
Enroll for free