Introduction to CI/CD on AWS.
In the evolving world of software development, CI/CD (Continuous Integration and Continuous Deployment) has become a foundational practice for delivering applications faster, safer, and more reliably.
When paired with cloud-native tools, CI/CD unlocks a new level of automation, efficiency, and scalability and this is where AWS (Amazon Web Services) truly excels.
As one of the leading cloud platforms, AWS provides a suite of fully managed services purpose-built for CI/CD pipelines, including AWS Code Pipeline, AWS Code Build, and AWS Code Deploy.
These services empower developers to automate every step of their software release process from pulling code from GitHub, running automated tests, building artifacts, to deploying applications in production environments all without managing any servers.
Code Pipeline serves as the orchestration engine for CI/CD on AWS. It integrates seamlessly with GitHub, AWS services, and third-party tools, allowing you to create workflows that trigger on every code push or pull request.
Whether you’re working with microservices, monoliths, or serverless architectures, CodePipeline helps ensure your application moves through testing, staging, and production environments in a consistent, repeatable way.
Next, there’s Code Build, AWS’s fully managed build service, which compiles your source code, runs tests, and produces deployable artifacts.
It supports a wide variety of build environments such as Node.js, Python, Java, .NET, and even custom Docker images.
All build processes are defined through a simple buildspec.yml file, making the build steps portable and version-controlled within your GitHub repository.
Following the build process, Code Deploy takes over deployment, pushing your code to EC2, Lambda, or ECS targets with support for blue/green deployments, canary releases, and rollback strategies all crucial features for maintaining high availability and minimizing risk in production.
For teams already using GitHub as their version control system, integrating AWS CI/CD tools is straightforward. Code Pipeline allows you to connect directly to your GitHub repositories, so every commit automatically triggers the pipeline, leading to continuous integration and rapid feedback loops.
This tight integration improves developer productivity and enforces consistent deployment standards across your environments.
What makes AWS’s CI/CD stack even more powerful is its serverless nature. None of the tools Code Pipeline, Code Build, or Code Deploy require provisioning or maintaining infrastructure.
They scale automatically with your workload, enabling startups and enterprises alike to focus on writing code instead of managing CI/CD servers.
Combined with other AWS services like CloudWatch for monitoring, IAM for secure role-based access, and S3 for storing artifacts, the entire process becomes robust, secure, and scalable.
Implementing CI/CD on AWS isn’t just about tools it’s a step toward adopting DevOps best practices. It fosters collaboration between development and operations, encourages frequent deployments, and reduces manual errors.
Whether you’re just getting started with cloud-native development or modernizing a legacy system, AWS’s CI/CD tools provide the foundation for building fast, reliable, and secure delivery pipelines.
With automation at the core and integration with popular tools like GitHub, AWS makes continuous delivery not just achievable, but efficient and cost-effective.
In this blog, we’ll walk through setting up a real-world pipeline using Code Pipeline, Code Build, and GitHub, so you can bring CI/CD into your AWS workflow with confidence.
Prerequisites
- AWS account
- GitHub repo with app code (Node.js, Python, etc.)
- IAM permissions for CodePipeline, CodeBuild
- Basic understanding of AWS console and services
Architecture Overview.
The architecture of a CI/CD pipeline on AWS is designed to automate and streamline the entire software delivery lifecycle, from source code to production deployment.
At its core, the pipeline leverages three key AWS services: Code Pipeline, Code Build, and Code Deploy, working together to form a cohesive, scalable, and fully managed DevOps workflow.
The pipeline begins with a source stage, typically integrated with GitHub, which acts as the version control system for your application’s codebase. When a developer pushes code to a GitHub repository, a webhook or polling mechanism triggers the Code Pipeline, initiating the automated workflow.
This integration ensures that every commit is continuously integrated, forming the foundation for continuous integration (CI).
After the source is fetched, the pipeline moves to the build stage, where Code Build steps in. Code Build is a serverless, fully managed build service that compiles source code, installs dependencies, runs tests, and produces artifacts.
These artifacts are typically packaged application binaries, containers, or configuration files and are stored in Amazon S3 for later stages.
The build process is defined using a buildspec.yml file, allowing teams to specify build instructions, environment variables, test commands, and output locations directly in the source repository. This enables consistent, repeatable builds across environments.
Code Build supports a wide range of runtime environments like Node.js, Python, Java, Go, and even custom Docker images giving teams maximum flexibility.
Once the build succeeds, the pipeline transitions to the deploy stage, where Code Deploy handles application deployment.
Code Deploy supports various deployment targets including EC2 instances, ECS (Elastic Container Service), and AWS Lambda, enabling both traditional and serverless workloads.
For production-critical environments, Code Deploy supports advanced deployment strategies such as blue/green deployments, canary releases, and automatic rollbacks, all of which enhance reliability and reduce downtime.
Deployment is typically driven by an AppSpec file that instructs Code Deploy on how to handle each phase before install, after install, validate service, and so on making deployments deterministic and controlled.
This architecture is event-driven and fully automated, embodying the principles of infrastructure as code and DevOps automation. Each component of the pipeline is loosely coupled, yet seamlessly integrated, enabling developers to independently modify stages or plug in custom logic.
For example, additional test stages, security scanning, or manual approval steps can be inserted between existing phases in Code Pipeline. Integration with IAM (Identity and Access Management) ensures each stage operates with least-privilege permissions, reinforcing security best practices.
The pipeline is also designed for observability and traceability. Each stage outputs logs to Amazon CloudWatch, helping teams diagnose failures quickly and monitor build or deploy metrics.
The use of S3 for artifact storage ensures artifacts are versioned and accessible across stages and environments. Moreover, because all services are fully managed, there’s no infrastructure to patch or scale AWS handles that automatically, letting your team focus on writing better code and releasing faster.
By combining GitHub with Code Pipeline, Code Build, and Code Deploy, this AWS CI/CD architecture achieves a fast, secure, and highly scalable deployment pipeline that can handle everything from a simple web app to a complex microservices platform.
Whether you’re deploying to a single Lambda function or orchestrating multi-service deployments on ECS, this architecture enables teams to release changes confidently, frequently, and automatically, aligning perfectly with modern DevOps practices and the needs of cloud-native development.
Step-by-Step Setup.
Setting up a CI/CD pipeline on AWS involves a sequence of structured steps using fully managed services such as AWS CodePipeline, CodeBuild, and optionally CodeDeploy, all of which integrate seamlessly with GitHub.
The first step is to prepare your GitHub repository, which will serve as the source stage of the pipeline. Your codebase should include not just the application logic but also a buildspec.yml file, which defines the build instructions for Code Build.
This YAML file contains directives for runtime versions, build phases, test commands, and output locations for artifacts. Storing this file in the root of your GitHub project ensures Code Build can automatically locate and execute it during the build stage.
Once the repository is ready, navigate to the AWS Management Console and open Code Pipeline to begin creating a new pipeline.
Assign it a descriptive name, and choose to create a new service role or use an existing IAM role with permissions for Code Pipeline, Code Build, S3, and other dependent services. In the source stage, choose GitHub as the provider, then authenticate your GitHub account using either OAuth or a GitHub App connection.
Select the repository and branch you want to track for example, main or develop. This configuration ensures that every commit or pull request triggers the CI/CD process, enabling continuous integration.
Next, you define the build stage. Create a new CodeBuild project and configure the environment. You can choose a managed image, such as aws/codebuild/standard:7.0, or use a custom Docker image stored in Amazon ECR.
Set the buildspec file path (usually buildspec.yml), and configure environment variables if needed. Ensure the project has an IAM role with access to S3 (for storing artifacts), logs (via CloudWatch), and any services it needs to interact with.
At runtime, Code Build will download the source from GitHub, install dependencies, run tests, and generate output artifacts typically stored in an S3 bucket configured during the build project setup. You can also enable build caching for faster builds.
With the build stage in place, move on to the optional but highly valuable deploy stage. Depending on your application’s architecture, you can deploy to EC2, ECS, or AWS Lambda using CodeDeploy.
This service supports advanced deployment strategies such as blue/green deployments, canary releases, and automatic rollbacks, which are crucial for minimizing downtime and ensuring high availability. To configure Code Deploy, you need to create an AppSpec file (appspec.yml) in your repo.
This file defines lifecycle hooks such as Before Install, After Install, and Validate Service, allowing you to control exactly what happens during each phase of deployment.
After configuring deployment, finalize your CodePipeline by connecting the build artifact output to the deploy input. CodePipeline automatically passes artifacts between stages, stored temporarily in S3, ensuring smooth transitions.
Once your pipeline is created, every push to the connected GitHub branch will trigger an automated run of the entire pipeline from pulling the source to building and testing it, and finally deploying it to the specified environment. This provides a fully automated CI/CD flow with minimal human intervention.
You can monitor pipeline execution via the Code Pipeline dashboard, view build logs in Code Build, and diagnose deployment issues in Code Deploy using CloudWatch Logs. For security, ensure all roles use least-privilege IAM policies, and audit actions via AWS CloudTrail.
You can also introduce approval stages, security scans, and test gates to customize the pipeline according to organizational DevOps standards.
The modular design of the AWS CI/CD stack allows you to start small with just Code Pipeline and Code Build and gradually expand to more advanced setups.
This step-by-step setup demonstrates how AWS CI/CD services work in harmony to support agile, cloud-native, and serverless application delivery.
Whether you’re deploying a static site, a containerized app on ECS, or a function on Lambda, this setup offers repeatability, automation, and robust integration with your development tools like GitHub.
Over time, you can scale this architecture by adding multi-environment pipelines (dev/staging/prod), integrating with external tools (like Slack or JIRA), or extending stages using Lambda functions for custom logic.
With this foundation, your team is now equipped to ship software faster, safer, and more reliably using the full power of AWS DevOps tooling.
Set Up Code Build.
Once the source stage of your Code Pipeline is configured to pull code from GitHub, the next critical step in building an effective CI/CD pipeline on AWS is setting up Code Build.
AWS Code Build is a fully managed, serverless build service that compiles source code, runs tests, and produces artifacts that can be used in later deployment stages.
Because it’s fully managed, you don’t need to provision or manage any infrastructure Code Build scales automatically based on your project’s needs, making it ideal for DevOps teams looking to streamline build automation.
The setup process begins in the AWS Console, where you create a new Code Build project and link it to your source repository, typically hosted on GitHub.
During this step, you can choose to authenticate with GitHub using OAuth or a GitHub App integration, allowing CodeBuild to access the exact branch that Code Pipeline is tracking.
The next configuration involves selecting the environment image. AWS provides managed build environments like aws/codebuild/standard:7.0, which support a wide array of programming languages such as Node.js, Python, Java, Ruby, .NET, and Go.
Alternatively, if you require more customization, you can supply a custom Docker image stored in Amazon ECR, giving you complete control over the build environment.
You must also define the runtime environment, set compute options, and configure environment variables that your build process might depend on. These could include deployment targets, API keys, or build flags.
All these settings are isolated per build, ensuring that each run is secure and repeatable.
One of the most important parts of setting up Code Build is the buildspec.yml file. This file should be committed to the root of your GitHub repository.
It outlines the instructions that Code Build will execute during the build phase. A simple buildspec.yml might include phases like install, pre_build, build, and post_build, where you define commands such as npm install, pytest, mvn package, or Docker build steps.
You can also configure output artifacts that Code Build packages and stores in Amazon S3 for use in subsequent stages of your pipeline, such as deployment with Code Deploy or integration testing with Lambda functions.
IAM configuration is another critical aspect of Code Build setup. Your build project must assume a service role that includes least-privilege permissions to access S3 buckets, CloudWatch logs, and any other AWS resources used during the build.
For example, if your build process involves pulling base images from ECR or uploading logs to CloudWatch, the IAM role must include those permissions.
AWS provides managed policies like AWS Code Build Developer Access as a starting point, but you should customize roles to match your project’s security requirements.
For visibility and troubleshooting, CloudWatch Logs are automatically integrated into every Code Build project. This lets you monitor real-time build output and diagnose issues like missing dependencies, failed tests, or incorrect command sequences.
You can even enable notification triggers via SNS or CloudWatch Alarms for build failures or long runtimes. The result is a fully observable, automated build system that integrates cleanly into your broader CI/CD workflow.
After completing the setup, your Code Pipeline will pass source artifacts from GitHub into Code Build, which then executes the instructions defined in your buildspec.yml, generates artifacts, and pushes them to S3.
This output is then used in the deploy stage, typically handled by Code Deploy, ECS, or Lambda, depending on your application architecture. Since Code Build is event-driven and stateless, it fits perfectly into any cloud-native CI/CD design.
Its flexibility, combined with AWS’s tight service integrations, makes it a powerful and scalable option for teams building everything from web apps and microservices to infrastructure modules and container images.
By setting up CodeBuild properly, you lay the groundwork for a robust, repeatable, and fully automated build process that accelerates development velocity and ensures consistency across environments.
Whether you’re deploying a monolith to EC2 or a serverless API to Lambda, Code Build makes continuous integration on AWS both practical and powerful.
Create the Pipeline.
Once your Code Build project is configured and tested, the next step in setting up a robust CI/CD pipeline on AWS is to create the actual pipeline using AWS Code Pipeline.
Code Pipeline is a fully managed orchestration service that automates the build, test, and deployment phases of your application release process.
It supports integration with GitHub, Code Build, Code Deploy, S3, Cloud Formation, Lambda, and many other AWS and third-party services, allowing you to design a highly customized and efficient DevOps workflow.
To begin, navigate to the AWS Console, open Code Pipeline, and click Create pipeline. Assign a name to your pipeline, and choose to either use an existing IAM service role or allow Code Pipeline to create one automatically with the required permissions to interact with other AWS services like Code Build, S3, and Code Deploy.
The pipeline begins with the source stage, where you select GitHub as your source provider. After authenticating via OAuth or GitHub App, select your repository and target branch (such as main or develop).
This will trigger the pipeline on every commit or pull request, enabling continuous integration. Once the pipeline is triggered, it pulls the latest code and packages it into a source artifact, which gets passed along to the next stage. You can choose to store these artifacts temporarily in an S3 bucket, ensuring that each pipeline execution is isolated and traceable.
Next, you define the build stage and point it to the Code Build project you created earlier. Code Pipeline will now use Code Build to compile code, run tests, and output build artifacts, which can include binaries, container images, or zipped files depending on your project’s structure.
These build outputs are passed as artifacts to subsequent stages. Code Pipeline also supports output artifact encryption, and you can apply KMS keys to enhance security. Logs for this stage are captured automatically in CloudWatch, helping developers diagnose issues and maintain visibility.
Following the build, the deploy stage comes into play. Here, you can use Code Deploy to deploy your application to EC2, Lambda, or ECS, depending on your infrastructure.
You’ll need to specify the deployment group, target environment, and optionally use a deployment strategy like blue/green, rolling, or canary deployment to minimize risk.
If you’re working with serverless applications, deploying to AWS Lambda can be automated through SAM or CloudFormation integration within the pipeline.
In the deploy stage, Code Pipeline uses App Spec files to control deployment behavior and hooks, offering fine-grained control over the process.
You can further customize the pipeline by inserting optional actions like manual approvals, security scans, or integration tests between any stages.
For example, before production deployment, you can add a manual approval action that pauses the pipeline until a human reviews the change and clicks “approve.”
This is especially valuable for teams following strict compliance or change control procedures. You can also integrate SNS notifications or Slack alerts to improve team awareness of pipeline events.
Another powerful feature of Code Pipeline is its ability to support multi-environment deployments. You can configure separate pipelines—or stages within one pipeline—for development, staging, and production environments, each gated by manual or automated quality checks.
These pipelines can also be defined and managed as infrastructure as code using tools like AWS CloudFormation, AWS CDK, or Terraform, making your CI/CD architecture version-controlled and easily reproducible.
Finally, after your pipeline is created, test it by pushing a change to your GitHub repo. You’ll see the pipeline execution flow through all stages—source, build, and deploy—automatically.
Each stage’s status is visible in the Code Pipeline dashboard, with detailed logs and diagnostics available in CloudWatch and the Code Build console.
By using Code Pipeline to create this flow, you automate your entire software delivery process, reduce manual errors, and accelerate release cycles.
This complete setup represents a modern, scalable, and secure CI/CD solution on AWS, fully integrated with your GitHub development workflow.
It empowers teams to build, test, and deploy applications with confidence—whether deploying microservices to ECS, Lambda functions to API Gateway, or full-stack web apps to EC2. With Code Pipeline at the center, your CI/CD process becomes predictable, repeatable, and resilient—delivering on the promise of cloud-native DevOps.
Triggering the Pipeline.
Once your CI/CD pipeline on AWS is fully configured with Code Pipeline, Code Build, and optionally Code Deploy, the next critical step is triggering the pipeline to initiate an automated pipeline execution.
The trigger mechanism starts at the source stage, which in most cases is connected to a GitHub repository. When a developer pushes code to the specified branch (like main or develop), a webhook or polling mechanism is activated.
This webhook sends a notification to Code Pipeline, signaling it to start a new run. This seamless integration with GitHub ensures that every commit is part of a continuous integration cycle, reducing manual intervention and aligning perfectly with DevOps best practices.
Upon receiving the trigger, Code Pipeline begins executing its defined stages. The source artifact is pulled from GitHub and stored in a temporary S3 bucket, marking the beginning of the pipeline.
The build stage then hands off the artifact to Code Build, which compiles the code, runs automated tests, and creates deployment-ready artifacts.
These build triggers are instantaneous and scalable, supporting parallel pipeline executions without provisioning any infrastructure. The process is event-driven, ensuring minimal latency between a code commit and pipeline start.
Next, if configured, the deploy stage is automatically triggered using Code Deploy or other targets like Lambda, EC2, or ECS, based on your architecture. For serverless applications, this could mean instantly deploying a new version of a function.
For containerized microservices, this might push a Docker image to ECS. If a manual approval stage is defined, the pipeline will pause and await a confirmation before proceeding to production, adding a layer of human oversight when needed.
To monitor these triggers and executions, CloudWatch Logs capture real-time output for every stage—source, build, and deploy—allowing developers to quickly debug any failures or delays.
Additionally, you can set up notifications via SNS or integrate with Slack to alert your team of pipeline events. All activity is securely logged and can be audited via AWS CloudTrail, and IAM roles ensure each service has the minimum necessary permissions.
By automating pipeline triggers through GitHub and AWS services, your workflow becomes not only faster but also more reliable and consistent.
Whether deploying infrastructure, a full-stack application, or microservices, triggering the pipeline with every code change guarantees that your product stays up to date, tested, and ready for production at all times. This event-driven model is a cornerstone of scalable, cloud-native CI/CD pipelines on AWS.
Monitoring and Logs.
Effective monitoring and logs are critical components of a successful CI/CD pipeline on AWS, providing the visibility and insights needed to ensure reliable and consistent deployments.
Once your pipeline built with Code Pipeline, Code Build, and optionally Code Deploy is in motion, you can track every step of the pipeline execution through integrated logging and monitoring tools like Amazon CloudWatch and AWS CloudTrail.
Each stage of the pipeline source, build, and deploy automatically generates logs that are pushed to CloudWatch Logs, allowing teams to analyze build output, deployment status, test results, and error messages in real time.
In the Code Build stage, every command defined in your buildspec.yml is logged line-by-line, giving developers full visibility into installation steps, unit tests, and artifact creation.
If a build fails due to syntax errors, failed dependencies, or misconfigured environment variables, the error output will appear in the build logs.
You can access these logs directly from the Code Build console, where each build job is listed with its corresponding status whether Succeeded, Failed, or In Progress and linked to detailed logs in CloudWatch. These logs can be filtered, searched, and archived for long-term retention.
During the deploy stage, services like Code Deploy push lifecycle hook logs (e.g., Before Install, After Install, Validate Service) to CloudWatch as well.
This is especially valuable when deploying to EC2, ECS, or Lambda, as it allows you to see precisely where a deployment failed whether in downloading the artifact from S3, starting a container in ECS, or executing a Lambda function.
For serverless applications, logs from AWS Lambda are also automatically sent to CloudWatch, offering seamless insight into execution outcomes.
To enhance observability, you can set up CloudWatch Alarms on key metrics such as build duration, failure counts, or deployment errors.
These alarms can trigger SNS notifications, alerting your team via email, SMS, or Slack when something goes wrong. For auditability and compliance, all API calls and pipeline actions are logged in AWS CloudTrail, allowing security teams to track who triggered what and when an essential feature in enterprise DevOps environments.
Permissions are managed through IAM roles, and only authorized users or services can access logs, view pipeline states, or read artifacts from S3.
With this level of control, teams can enforce strict access policies while still maintaining high automation and agility. Together, Cloud Watch, Code Pipeline, Code Build, and Code Deploy provide a tightly integrated monitoring and logging ecosystem that supports scalable, reliable, and secure CI/CD workflows.
Whether you’re debugging a failed build, investigating a deployment error, or simply validating that all stages executed successfully, AWS logging and monitoring tools give you the operational intelligence you need.
In modern CI/CD pipelines, visibility is not optional it’s a necessity for stability, scalability, and continuous improvement.
Common Pitfalls & Fixes.
Even with powerful tools like Code Pipeline, Code Build, and Code Deploy, developers often encounter common pitfalls while building or running a CI/CD pipeline on AWS.
One frequent issue arises from misconfigured IAM roles. Each pipeline stage source, build, and deploy requires specific permissions to access services like S3, CloudWatch, and GitHub.
If roles lack necessary policies or include overly restrictive permissions, the pipeline may fail silently or throw access denied errors.
To fix this, ensure all IAM roles follow the principle of least privilege while including required actions like s3:GetObject, code build: Start Build, and code deploy: Create Deployment.
Another common pitfall lies in the buildspec.yml file, which guides the CodeBuild stage. Improper YAML syntax, missing phases (like install or build), or incorrect artifact paths can cause builds to fail.
These issues often go unnoticed until a pipeline execution fails. Always validate the buildspec.yml locally and keep commands minimal and testable. Use CloudWatch Logs to trace each command’s output and locate failures during the build.
Also, be sure to specify correct environment variables, as these can control build targets, secrets, or deployment behavior. Undefined or incorrectly scoped variables can lead to runtime errors that are difficult to debug.
Another area developers struggle with is artifact handling. If CodeBuild is configured to produce artifacts but the output directory or filenames are incorrect, the deploy stage in CodePipeline will fail to locate them.
Ensure the paths in artifacts.files match the build output exactly, and verify that the destination S3 bucket exists and is correctly referenced. Similarly, when using Code Deploy for services on EC2, ECS, or Lambda, errors in the App Spec file can break deployments.
For example, incorrect lifecycle hook names or missing scripts will result in failed deployments. Always test deployment scripts in isolation and confirm that hooks like Before Install or After Install execute successfully.
Another pitfall is insufficient logging and monitoring. Teams often forget to enable CloudWatch Logs for Code Build or Code Deploy, making troubleshooting much harder.
Enable logging from the start and configure CloudWatch Alarms to detect long build times or frequent failures. For teams relying on GitHub webhooks, connection timeouts or incorrect webhook URLs can silently prevent the source stage from triggering. Double-check your GitHub integration and test with manual commits to verify automation.
Lastly, skipping manual approval stages in sensitive production pipelines can lead to untested or faulty code being deployed. While automation is key to DevOps, certain environments benefit from a manual review step to prevent costly downtime.
Similarly, failing to configure rollback options in Code Deploy can result in prolonged outages. Use blue/green deployments, canary releases, and automatic rollback features to reduce impact when something goes wrong.
By being aware of these pitfalls and leveraging AWS logging, proper IAM setup, and testable pipeline components you can build a more resilient, secure, and scalable CI/CD pipeline. With proper planning and attention to detail, these common issues become easy to anticipate and fix, strengthening the foundation of your cloud-native DevOps workflow.
Final Thoughts & Next Steps
- CI/CD with CodePipeline and CodeBuild is fully managed, scalable, and native to AWS.
- Consider adding:
- Unit tests
- Security scanning (e.g., CodeGuru or Snyk)
- Multi-environment deployments (dev/staging/prod)
Conclusion.
In conclusion, building a robust CI/CD pipeline on AWS using Code Pipeline, Code Build, and GitHub empowers development teams to automate their entire software delivery process seamlessly.
This integration not only accelerates the pace of innovation by enabling continuous integration and continuous deployment but also ensures consistency, security, and scalability across all stages from source code management to build and deployment.
By leveraging AWS’s fully managed, serverless services, teams can reduce operational overhead while gaining deep visibility through integrated monitoring and logging tools like CloudWatch.
Whether you’re deploying microservices on ECS, serverless functions with Lambda, or traditional applications on EC2, this approach adapts effortlessly to your architecture and DevOps workflows.
Embracing this automated pipeline lays a strong foundation for faster releases, higher quality software, and a more agile development lifecycle in today’s cloud-first world.
