Amazon EKS - Observability with Prometheus and Grafana

Lab Steps

Logging in to the Amazon Web Services Console
Connecting to the Virtual Machine using EC2 Instance Connect
Reviewing Amazon EKS Resources Automatically Created
Installing Kubernetes Management Tools and Utilities
Observability using Prometheus and Grafana

The hands-on lab is part of this learning path

Ready for the real environment experience?

Time Limit2h


When it comes to running and operating production-grade EKS managed Kubernetes clusters, monitoring and alerting are considered essential components of an enterprise Kubernetes observability stack. 

In this hands-on lab, you'll learn how to set up and use the following essential monitoring applications:

You'll learn how to integrate these monitoring applications together into an effective and cohesive monitoring solution.

Learning Objectives

Upon completion of this Lab, you will be able to:

  • Deploy and instrument a sample Python Flask web-based API into Kubernetes, instrumented to provide metrics which will be collected by Prometheus and displayed within Grafana
  • Install and configure Prometheus into Kubernetes using Helm
  • Setup Prometheus for service discovery
  • Install and configure Grafana into Kubernetes using Helm
  • Import pre-built Grafana dashboards for real-time visualisations

Intended Audience

This lab is intended for:

  • Kubernetes practitioners
  • DevOps Engineers
  • SREs

Lab Prerequisites

You should be familiar with:

  • Basic Linux command line administration
  • Basic Kubernetes and Container-based concepts

Lab Environment

This Lab will start with the following AWS resources provisioned automatically for you:

  • 1 x EKS cluster - Cluster-1 - provides a fully functional Kubernetes cluster 
    • 1 x NodeGroup
      • 2 x EC2 Worker Nodes
  • 2 x EC2 instances
    • eks.launch.instance - used to launch the EKS cluster
    • cloudacademylabs - used to provide an SSH based terminal


About the Author
Learning paths56

Jeremy is a Content Lead Architect and DevOps SME here at Cloud Academy where he specializes in developing DevOps technical training documentation.

He has a strong background in software engineering, and has been coding with various languages, frameworks, and systems for the past 25+ years. In recent times, Jeremy has been focused on DevOps, Cloud (AWS, GCP, Azure), Security, Kubernetes, and Machine Learning.

Jeremy holds professional certifications for AWS, GCP, and Kubernetes.