Amazon EKS - Install the AWS Load Balancer Controller and Create an Ingress Resource (ALB)

Lab Steps

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Logging in to the Amazon Web Services Console
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Connecting to the Virtual Machine using EC2 Instance Connect
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Reviewing Amazon EKS Resources Automatically Created
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Installing Kubernetes Management Tools and Utilities
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Review AWS Load Balancer Controller Config Prerequisites
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Deploy AWS Load Balancer Controller
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Deploy and Expose the Web App

Ready for the real environment experience?

DifficultyIntermediate
Time Limit1h 30m
Students40
Ratings
5/5
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Description

The AWS Load Balancer Controller is used to help manage and control the provisioning of ALBs (Ingress resources) and NLBs (Service resources - type LoadBalancer).

In this Lab scenario, you'll learn how to deploy the AWS Load Balancer Controller into the lab provided EKS cluster using Helm. You'll then deploy a sample web app and configure external public access to it via an Ingress cluster resource.

Learning Objectives

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

  • Deploy and configure the AWS Load Balancer Controller into an EKS cluster
  • Deploy a sample web app and expose it publicly using an Ingress resource

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

Consider taking the following courseware in preparation for this lab:

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
      • 1 x EC2 Worker Node
  • 2 x EC2 instances
    • eks.launch.instance - used to launch the EKS cluster
    • cloudacademylabs - used to provide an SSH based terminal

 

Environment before
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Environment after
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About the Author
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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, Terraform, Kubernetes (CKA, CKAD, CKS).