Infrastructure as Code changes the way cloud environments are built, tested and released. Terraform provides a declarative model for creating, updating and versioning infrastructure in a secure and repeatable manner. AWS CodePipeline orchestrates CI/CD workflow by connecting source providers, build systems and deployment targets into an automated pipeline. It is a solid choice if you are already invested in the AWS ecosystem and want everything under one roof.
Combining CodePipeline with Terraform addresses the operational friction of manual infrastructure changes. Setting up a pipeline through the console is painful with lots of clicking, lots of IAM role creation and zero reproducibility. Terraform fixes all of that by allowing the pipeline itself to be managed as code.
Core Concepts
AWS CodePipeline is a fully managed CI/CD service provided by Amazon Web Services which enables automation of the steps required to release software changes.
Understanding of primary terminologies is central to the pattern.
- AWS CodePipeline: It provides an immersible service that automates some processes like build, testing, and deployment of a more qualitative path so that it can release processes.
- Terraform: It is an open-source infrastructure software tool which is created by HashiCorp that can create and manage resources which are on Cloud Platform with the help of APIs.
- CI/CD Pipeline: It is a CI/CD pipeline that contains a series of steps which helps in automating the process of integrating code.
AWS CodePipeline is a continuous integration and continuous delivery service which is provided by Amazon Web Services that leads to automate the process of building, testing, and deploying the applications as CodePipeline enables creation of pipelines that define the steps and actions that are required to take the code from a source repository through various stages of testing and deployment, and ultimately to production.
By using CodePipeline you can design pipelines in order to integrate with other AWS services like AWS CodeBuild and can be used for building the application.
Pipeline Stages for Terraform Validation
A pattern shows how to test HashiCorp Terraform configurations by using a continuous integration and continuous delivery pipeline deployed by AWS CodePipeline.
Terraform is a command-line interface application that helps you use code to provision and manage cloud infrastructure and resources. The solution provided creates a CI/CD pipeline that helps validate the integrity of Terraform configurations by running five CodePipeline stages.
The created pipeline uses best practices for infrastructure validation and has the below stages.
- validate
- plan
- apply
- destroy
The scope of the pattern is to provide a guide and ready to use terraform configurations to setup validation pipelines with end-to-end tests based on AWS CodePipeline, AWS CodeBuild, AWS CodeCommit and Terraform.
Stage breakdown
The five-stage validation pipeline is described as follows:
checkout
pulls the Terraform configuration that you are testing from an AWS CodeCommit repository.validate
runs infrastructure as code validation tools, including tfsec, TFLint, and checkov. The stage also runs the following Terraform IaC validation commands: terraform validate and terraform fmt.plan
shows what changes will be applied to the infrastructure if the Terraform configuration is applied.apply
uses the generated plan to provision the required infrastructure in a test environment.destroy
removes the test infrastructure that was created during the apply stage.
A typical CodePipeline has three or four stages.
| Stage | Purpose | Key Actions |
|---|---|---|
| checkout | Source retrieval | Pulls Terraform configuration from AWS CodeCommit repository |
| validate | IaC validation | Runs tfsec, TFLint, checkov, terraform validate, terraform fmt |
| plan | Change preview | Creates execution plan, preview of changes Terraform plans to make |
| apply | Test deployment | Uses plan to provision infrastructure in test account |
| destroy | Cleanup | Destroys infrastructure created in apply stage |
This pattern approach deploys AWS CodePipeline into one AWS account and AWS Region only.
Validation Tooling and Security Scans
Validation focuses on Terraform IaC validation tools and commands such as terraform validate, terraform format, tfsec, tflint and checkov.
In the DevOps Pipeline Accelerator, building blocks for AWS CodePipeline are used to create accelerators for Terraform and CloudFormation IaC.
Standardized pipeline structure is combined with reusable stages and jobs and integrated tools for security scans.
Tools referenced for IaC validation include:
cfn-lint
is a linter that checks CloudFormation YAML or JSON templates against the AWS CloudFormation resource specification. It also performs other checks, such as checking for valid values for resource properties and adherence to best practices.cfn_nag
is an open source tool that identifies potential security issues in CloudFormation templates by searching for patterns.Checkov
is a static code-analysis tool that checks IaC for security and compliance misconfigurations.TFLint
is a linter that checks Terraform code for potential errors and adherence to best practices
The validate stage focuses on terraform IaC validation tools and commands such as terraform validate, terraform format, tfsec, tflint and checkov.
Architecture Patterns and Code as Code
AWS CodePipeline orchestrates your CI/CD workflow by connecting source providers, build systems, and deployment targets into an automated pipeline. It's a solid choice if you're already invested in the AWS ecosystem and want everything under one roof. Setting it up through the console is painful though - lots of clicking, lots of IAM role creation, and zero reproducibility. Terraform fixes all of that.
This guide walks through building a complete pipeline from source through build to deployment, all managed as code.
The pattern provides a guide and ready to use terraform configurations to setup validation pipelines with end-to-end tests based on AWS CodePipeline, AWS CodeBuild, AWS CodeCommit and Terraform.
In the accelerator model:
- pipeline-modules – This folder contains the code for deploying the standardized pipeline structure.
- shared – This folder contains ready-to-use buildspec files for the DPA stages and jobs.
Prerequisites for using the accelerator include:
- An active AWS account
- Permissions to provision resources using IaC templates
- Permissions to create AWS CodeCommit repositories and CodePipeline components
Planning, Approval, and Apply Workflow
Automation of Terraform deployments with AWS CodePipeline delivers benefits such as:
- Simplify deployments: Streamline Terraform changes through automated pipelines.
- Reduce errors: Ensure consistency and reliability with defined stages.
- Faster build and deploy: Minimize manual intervention for quicker turnarounds.
- Increased auditability: Track changes and identify issues more easily.
A practical workflow to automate deployment of Terraform code using AWS CodePipeline and CodeBuild splits work into stages.
The pipeline can allow any code committed to the GitHub Repository to be deployed into AWS Automatically, and should only need minimal maintenance as it should not change on the same frequency as the API Gateway / Lambda Code.
To ensure we don't just blindly deploy code that hasn't been checked, the flow is split out into stages.
The stages include:
- CodePipeline
- Download Source Code from API Gateway Repository
- Run a Planning Step in AWS CodeBuild.
- Download and Install Terraform
- Initialise the Terraform Environment with an S3 Backend
- Run the Terraform Plan, and save the output to an Artifact
- Send an Email via SNS to say the pipeline is awaiting approval
- Await Manual Approval
- Run an Apply Step in AWS CodeBuild.
- Download and Install Terraform
- Initialise the Terraform Environment with an S3 Backend
- Run the Terraform Apply using the Artifact from the Planning stage
For the main terraform setup, prerequisites include backend and provider configuration.
```
terraform {
required_providers {
aws = {
source = "hashicorp/aws"
version = "~> 4.0"
}
}
S3 backend configuration
backend "s3" {
bucket =
}
}
```
The plan stage creates an execution plan, which lets you preview the changes that Terraform plans to make to your infrastructure. The apply stage uses the plan created above to provision the infrastructure in the test account.
Tooling Integration and Best Practices
Integration with AWS CodeBuild is common. CodePipeline enables creation of pipelines that define steps and actions required to take code from a source repository through various stages of testing and deployment.
The pattern for validation pipelines uses AWS CodePipeline, AWS CodeBuild, AWS CodeCommit and Terraform together.
Best practice validation pipeline stages:
- validate - This stage focuses on terraform IaC validation tools and commands such as terraform validate, terraform format, tfsec, tflint and checkov
- plan - This stage creates an execution plan, which lets you preview the changes that Terraform plans to make to your infrastructure.
- apply - This stage uses the plan created above to provision the infrastructure in the test account.
- destroy - This stage destroys the infrastructure created in the above stage
The solution creates a CI/CD pipeline that helps you validate the integrity of your Terraform configurations by running the stages described.
Conclusion
AWS CodePipeline for Terraform provides a repeatable path from source commit to validated infrastructure. The combination of CodePipeline orchestration, CodeBuild execution and Terraform as the IaC engine delivers automated validation, safe change preview and controlled deployment.
The five-stage validation pattern of checkout, validate, plan, apply and destroy establishes guardrails for infrastructure code. Validation with tfsec, TFLint and Checkov combined with terraform validate and formatting commands ensures configurations are syntactically correct and aligned with security and best practice checks before any change is previewed.
Planning as a distinct stage with artifact generation enables review and manual approval before apply. Using an S3 backend for Terraform state and storing plan artifacts preserves auditability and reproducibility.
Managing the pipeline itself with Terraform closes the loop on reproducibility. The pipeline can be defined as code, versioned, and updated through the same CI/CD process it enforces. This approach reduces manual setup pain, eliminates console-driven IAM sprawl, and makes the entire delivery flow auditable and portable across accounts.
Organizations already invested in AWS benefit from keeping source, build and deployment under one roof with CodePipeline while leveraging Terraform for infrastructure definition and lifecycle management. The result is simplified deployments, reduced errors, faster build and deploy cycles and increased auditability across infrastructure changes.