Building a Packer Golden Image Using AWS Build Pipeline

Introduction

In modern infrastructure and application delivery workflows, golden images play an important role in ensuring consistency, security, and repeatability. A golden image is a pre-baked machine image that contains the required operating system, configurations, and application dependencies, making it easier to launch new instances with a known-good baseline.

This article walks through how to set up an AWS build pipeline to automate the creation of golden images using HashiCorp Packer.

Body

Why Use a Golden Image?

Golden images help solve common operational challenges:

•   Consistency: Every instance launched has the same configuration and software versions.
•   Faster provisioning: Reduces time spent on bootstrapping and installing dependencies.
•   Improved security: Images can be patched, scanned, and hardened before use.
•   Auditability:Provides a clear versioned history of changes to infrastructure images.

Components of the Pipeline

To build an automated golden image pipeline on AWS, the following services are commonly used:

AWS CodeCommit (or GitHub/GitLab/Bitbucket)

Stores your Packer templates and configuration files.

AWS CodePipeline

Orchestrates the overall workflow—triggers the build when changes are committed.

AWS CodeBuild

Executes the Packer build commands and provisions the AMI.

HashiCorp Packer

Creates the Amazon Machine Image (AMI) based on the configuration.

Amazon EC2

Runs the resulting AMI as a golden image for applications.

High-Level Workflow

Commit Changes

Developers push changes (e.g., updated dependencies, OS patches) to the Git repository containing the Packer template (.json or HCL file).

Pipeline Trigger

CodePipeline detects the change and triggers a new pipeline execution.

Build Stage

CodeBuild runs a buildspec file that installs Packer and executes the build command.
Packer provisions a temporary EC2 instance, installs the application, applies configurations, and generates a new AMI.

Post-Build Actions

Optionally, run automated tests to validate the AMI.
Tag the AMI with version details, date, and application name for easy tracking.
(Optional) Publish AMI IDs to Parameter Store or Secrets Manager for use by other services.

  Example: CodeBuild Buildspec

A typical buildspec.yml for Packer might look like this:
version: 0.2

phases:
install:
commands:
– curl -fsSL https://apt.releases.hashicorp.com/gpg | sudo apt-key add –
– sudo apt-add-repository “deb [arch=amd64] https://apt.releases.hashicorp.com $(lsb_release -cs) main”
– sudo apt-get update && sudo apt-get install -y packer
build:
commands:
– packer init .
– packer validate golden-image.pkr.hcl
– packer build golden-image.pkr.hcl
artifacts:
files:
– ‘**/*’
This script installs Packer, validates the template, and builds the golden image.

Best Practices:

• Version your images: Use semantic versioning or date-based tagging.
  Automated testing: Validate that the AMI boots correctly and required services are running.
  Security scanning: Integrate vulnerability scanners into the pipeline to ensure hardened images.
  Parameterization: Use variables in Packer to customize builds per environment (e.g., staging, production).

Conclusion

By integrating Packer with an AWS build pipeline, teams can automate the creation of golden images in a repeatable and secure manner. This approach reduces manual intervention, minimizes configuration drift, and provides a standardized foundation for application deployment across environments.