Automating your deployments: An introduction to CI/CD pipelines on AWS

Introduction

Every time a developer finishes writing a new feature, the code needs to be tested, packaged, and sent to the server. Doing this process manually by connecting to a server, uploading files, and restarting services is slow, repetitive, and highly prone to human error. This is where CI/CD, which stands for Continuous Integration and Continuous Delivery, comes into play to automate every single step from the moment code is saved to when it goes live.

Understanding Continuous Integration and Continuous Delivery

Continuous Integration is the practice of automatically merging code changes from multiple developers into a single central repository. Every time code is pushed, automated tests run to ensure nothing is broken.

Continuous Delivery takes over right after that. It automatically picks up that tested code and deploys it to the staging or production environments. The goal is to make software releases quick, predictable, and frequent.

The core AWS services for building a pipeline

AWS provides a set of tools designed specifically for this workflow. The first one is AWS CodeCommit, which is a secure, managed source control service where you store your code, similar to GitHub.

The second tool is AWS CodeBuild. It acts as a temporary server that compiles your source code, runs your automated unit tests, and produces ready to deploy software packages.

The third tool is AWS CodeDeploy, which automates the actual deployment of your software to various computing services like Amazon EC2 instances, AWS Lambda functions, or ECS containers.

Finally, AWS CodePipeline is the orchestrator. It connects all these tools together, capturing code changes from your repository, passing them to CodeBuild, and then sending them to CodeDeploy automatically.

How an automated pipeline works in practice

The workflow begins when a engineer pushes code to a specific branch in the repository. AWS CodePipeline constantly monitors this repository and instantly detects the update, triggering the first stage of the process.

In the next phase, CodePipeline sends the updated code to CodeBuild. CodeBuild reads a configuration file in the project to download the necessary tools, install dependencies, and run all test scripts. If a single test fails, the entire process stops immediately, and the developer is notified.

If all tests pass, the pipeline moves to the deployment phase. CodeDeploy takes the approved package and updates the servers using safe strategies, such as replacing old instances gradually to ensure the website or application never goes offline during the update.

Conclusion and next steps

Implementing an automated pipeline is a turning point for any technology team. It shifts the focus from managing servers and deployments to creating actual value through code.

As next steps to practice this concept, try exploring the AWS Console and creating a simple pipeline that connects a GitHub repository to an AWS Elastic Beanstalk environment.