/DevOps-The-Hard-Way-AWS

This repository contains free labs for setting up an entire workflow and DevOps environment from a real-world perspective in AWS

Primary LanguagePythonMIT LicenseMIT

DevOps-The-Hard-Way-AWS

This tutorial contains a full, real-world solution for setting up an environment that is using DevOps technologies and practices for deploying apps and cloud services/cloud infrastructure to AWS.

The repository contains free labs, documentation, diagrams, and docs for setting up an entire workflow and DevOps environment from a real-world perspective in AWS.

DevOps Scenario

The scenario that you're currently facing is you work in an organization that is very monolithic. There is a ton of bare metal, virtualization, manual deployments of applications, and old school practices based on the current teams knowledge of IT.

You're brought in to the company and team to make things more modern so the organization can not only succeed, but stay ahead of their competition. Management now understands the needs and complexity that comes with staying ahead of their competition and they know that they need to. Otherwise, the organization will fall...

DevOps Solution

The solution is to deploy the Uber API for the sign-up page. Currently this solution is sitting on a bunch of baremetal, but it's time to sprinkle a little DevOps on it.

As a DevOps Engineer, you're more or less (most likely) not writing the app, but instead, deploying it. That's why you're not writing your own app in this tutorial.

Full Disclosure - I did have to edit this app a bit from Uber to make it compatible with Python3. You can find the repo here:

https://github.com/AdminTurnedDevOps/Python-Sample-Application

Technology Details

You will be using the following technologies and platforms to set up a DevOps environment.

  1. AWS
    • AWS will be used to host the application, cloud infrastructure, and any other services we may need to ensure the Uber app is deployed properly.
  2. GitHub
    • To store the application and infrastructure/automation code
  3. Python
    • Python will be used for the Uber app (it is written in Python) and some automation efforts that aren't in Terraform.
  4. Terraform
    • Create an S3 bucket to store Terraform State files
    • Create an AWS ECR repository with Terraform
    • Create an EKS cluster
  5. Docker
    • Create a Docker image
    • Store the Docker image in AWS ECR
  6. Kubernetes
    • To run the Docker image that's created for the containerized Uber app. Kubernetes, in this case, EKS, will be used to orchestrate the container.
  7. CI/CD
    • Use GitHub Actions to create an EKS cluster
  8. Automated testing
    • Testing Terraform code with Checkov

Labs

  1. Prerequisites
  2. VPC - When running EKS, it requires specific networking. Because all environments will most likely be different, there's a CloudFormation template for this exact purpose.
  3. AWS:
  4. Terraform - The purpose of the Terraform section is to create all of the AWS cloud services you'll need from an environment/infrastructure perspective to run the Uber application.
  5. Docker - The purpose of the Docker section is to create a Docker image from the app that the organization is running on-prem (the uber app), containerize it, and store the container inside of a container repository. For the container repo, you'll use AWS ECR.
  6. Kubernetes - The purpose of the Kubernetes section is to connect to EKS locally and to write the Kubernetes manifest to deploy the Python Uber app.
  7. Automated Testing - The purpose of the Automation Testing section is to ensure that all of the Terraform code is performing as it should be from a policy, security, and static code analysis perspective.
  8. CICD - The purpose of this section is to automatically create an EKS cluster with CICD using GitHub Actions