How to Set Up Automated Deployment in AWS: A Full-Stack Developer‘s Guide

Automating deployment workflows is essential for efficiently shipping software at scale. This comprehensive 2600+ word guide from a full-stack developer‘s lens covers step-by-step instructions, best practices, tools comparison, and expert tips for setting up continuous integration (CI) and continuous delivery (CD) pipelines on Amazon Web Services (AWS).

Why Automation Matters

The ability to rapidly respond to customer feedback and iterate on software is critical for developer productivity and remaining competitive. However, manual build, test and deployment workflows significantly slow down release velocity.

As per State of DevOps Report by Google Cloud, high-performing engineering teams deploy 146 times more frequently than their low-performing counterparts.

Automation is the key driver behind this staggering difference:

Additionally, automation leads to 60x faster recovery from incidents and 24x more frequent code deployments, as per research covered in DORA‘s 2021 State of DevOps Report .

Clearly, mastering CI/CD and release automation needs to a top priority for technology leaders looking to accelerate innovation velocity.

Prerequisites

Before diving deeper, readers should have:

• Familiarity with Python, JavaScript or any other programming language
• Understanding of cloud infrastructure concepts
• Basic experience with Git version control
• Linux shell commands and scripting basics
• AWS account configured with admin access

Now let‘s explore how to leverage AWS services like CodeCommit, CodeBuild, CodeDeploy etc. to set up robust deployment pipelines.

Step 1 – Configuring AWS Resources

We need to provision some core resources in AWS as foundation before proceeding:

Creating a Secure IAM User

Log into the AWS console and navigate to the Identity and Access Management (IAM) page.

Here, create a new user with programmatic access enabled to generate an access key and secret token.

Attach the built-in AdministratorAccess policy to start out till you define custom permissions tailored to the app‘s needs. This will grant full access across AWS.

Tip: In real-world scenarios, always practice principle of least privilege by assigning restrictive policies.

Setting Up VPC Components

Under the Virtual Private Cloud (VPC) dashboard, create these core networking resources:

Security Groups: Define firewall rules governing network traffic for resources. Open ports 80, 443 and 22.

Key Pair: Generate a SSH key-pair allowing authentication connections to EC2 instances.

Make a note of the security group IDs and save key pairs securely.

Step 2 – Continuous Integration Pipeline

The CI pipeline runs automation tasks like build, test, package whenever developers commit code changes. Popular CI tools on AWS include:

We will leverage GitHub Actions for hands-on demonstration:

Configuring Secrets

Go to the GitHub repository Settings and add encrypted secrets like AWS keys, database passwords etc. These will be passed to workflows securely.

Defining CI Workflow

Create .github/workflows/ci-cd.yaml file to define automation steps:

on: 
  push:  
    branches:
      - main

jobs:

  build_and_test:   

    runs-on: ubuntu-latest

    steps:
    - uses: actions/checkout@v3
    - name: Set up Python  
      uses: actions/setup-python@v3
    - name: Install dependencies  
      run: |         
        python -m pip install --upgrade pip  
        pip install -r requirements.txt
    - name: Run tests
      run: |
        pip install pytest 
        pytest 
    - name: Build container image
      env:
        AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}  
        AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
      run: |             
        # Build container image
        # Push image to ECR repo   

This installs app dependencies, runs tests and builds images. Additional checks like static analysis, coverage reporting etc. can be added to enhance quality gates.

Step 3 – Continuous Delivery Pipeline

The CD pipeline picks up the CI artifacts and automatically releases to different environments.

We will deploy onto EC2 instances using AWS CodeDeploy:

Set up IAM Permissions

Create an IAM role like AWSCodeDeployRole permitting access to EC2, S3 and CloudWatch. This is needed by CodeDeploy.

Install CodeDeploy Agent

Add the install script in EC2 User Data during instance creation so hosts are prepped for deployment:

#!/bin/bash
yum update –y
yum install ruby
cd /home/ec2-user  
wget https://aws-codedeploy-us-east-2.s3.us-east-2.amazonaws.com/latest/codedeploy-agent.noarch.rpm
yum install codedeploy-agent -y
service codedeploy-agent start  

Configure AppSpec File

The AppSpec outline deployment steps like starting server, validating health checks etc.

Example appspec.yml:

version: 1.0
os: linux

hooks:
  BeforeInstall:
    - location: scripts/install_dependencies  

  ApplicationStart:  
    - location: scripts/start_server

# Additional hooks for lifecycle events   

Set up CodeDeploy Application

Inside CodeDeploy console, create an application with a unique name and EC2 compute type.

Configure Deployment Group

Next, set up a deployment group mapped to the application. Define configuration details:

• IAM instance profile
• Auto Scaling groups / EC2 tags
• Security groups and roles
• Key pairs for SSH

Add Deploy Stage to CI Pipeline

Extend .github/workflows/ci-cd.yaml with a deploy job triggered after CI success:

deploy:

  # Run only after CI passes  
  needs: build_and_test

  steps:  
    - name: Deploy app 
      env:
        AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}  
        AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
      run: |           
        aws deploy create-deployment  
          --application my_app
          --deployment-config CodeDeployDefault.OneAtATime  
          --deployment-group-name my_deployment_group
          --s3-location bucket=my-artifacts,bundleType=zip,key=app.zip 

This leverages CodeDeploy to rolling out builds from CI onto the infrastructure.

Best Practices for Release Automation

Beyond setting up tools, applying DevOps principles and maturity models is crucial for optimization. Here are 8 top recommendations based on my experiences:

1. Progressive Exposure to Mitigate Risks

Incrementally roll out builds across internal test, staging, and production instead of big bang releases. This allows assessing quality before exposing to users.

2. Implement Manual Approvals and Health Checks

Add approval checkpoints before promotion across stages. Confirm readiness through metrics like application load, uptime etc.

3. Leverage Blue/Green and Canary Deployments

Route a portion of traffic to new version alongside old one. This offers safe evaluation and ability to rollback easily.

4. Containerize Apps for Portability

Package apps inside containers using Docker for reliable deployment across diverse platforms and environments.

5. Monitor Dashboards Closely

Watch key DevOps metrics around lead time, deployment frequency, time to restore service etc. Optimization depends on data insights.

6. Validate End User Experience

Check app behavior from customer lens after releases through exploratory testing instead of only unit tests.

7. Automate Predictable Checks; Manual Unpredictable Ones

Special case validations are hard to automate. Leave room for human intelligence alongside bots.

8. Standardize Deployments on Infrastructure as Code

Manage cloud resources programmatically through templatized configuration files checked into source control.

Comparison of Popular CI/CD Tools on AWS

Here is a high-level features analysis on options beyond GitHub Actions:

Evaluate time to productivity, flexibility for customization, collaboration functionality and cost based on use-case for picking the right solutions.

Final Thoughts

In closing, setting up automated CI/CD is invaluable for achieving rapid innovation, reduced risks and reliability simultaneously amidst modern digital landscape.

As part of ongoing DevOps adoption journey, start small by picking one pipeline component at a time instead of a big bang overhaul. Incrementally expand scope to cover additional flows spanning plan to develop, build, test and deploy.

Refer to the 17 practices outlined in SAFe‘s DevOps culture transformation roadmap for strategic guidance.

What aspects of release automation have you found most valuable in your context? Are you leveraging AWS services or open source toolchains? Please share your experiences and feedback below!