Skip to main content

2 posts tagged with "ci-cd"

View All Tags

· 3 min read
Saurav Gopinath Panda

The world of cloud computing is constantly changing, and automation is an essential component in making infrastructure management more efficient and less prone to errors. By combining AWS Lambda, a serverless computing service, with the power of Terraform, an open-source infrastructure as a code software tool, you can significantly simplify this process. In this blog post, we'll explore a Python script that is designed to automate Terraform plan applications using AWS Lambda.

Understanding the Code

The Python script we're discussing is structured to run within an AWS Lambda environment. It's designed to trigger Terraform plans stored in an AWS S3 bucket, making infrastructure changes both automated and easily manageable.

The script starts by defining the path to the Terraform executable. Currently, we have a terraform binary executable (1.5.7) downloaded for amd64.

Set Up

To get started, clone the repository at Once you've done that, run bash to create a zip file for the lambda function. This file will be named ''.

Next, head over to the AWS Console and create a lambda function with amd64. Upload the zip file via the console.

Upload Lambda Zip

In the Configuration page, set the memory to 512 MB and timeout to 15 minutes (as the build time varies depending on your system).

Update Lambda Config

Once that's done, update the lambda function's role and add a new inline IAM policy. You can find this policy in the 'iam_policy.json' file.

Update Lambda IAM

That's it! You're now ready to use the lambda function to run terraform executions.

The Process

When the Lambda function is triggered, it follows these steps:

  1. Extracts Event Data: It reads the S3 bucket name and the Terraform file path from the event.

  2. Downloads the Terraform File: The specified Terraform file is downloaded from the S3 bucket.

  3. Executes Terraform Commands: It initializes and applies the Terraform plan using the run_command function.

  4. Handles Responses: Finally, it returns a response indicating the success or failure of the operation.

Use Cases

This automation script is particularly useful in scenarios such as:

  • Continuous Deployment: Automatically apply infrastructure changes as part of a CI/CD pipeline.
  • Scheduled Infrastructure Updates: Use AWS CloudWatch Events to trigger this Lambda function on a schedule.
  • Event-Driven Infrastructure Changes: Trigger infrastructure modifications in response to specific AWS events.


  • Scalability: AWS Lambda can handle varying loads, making this solution scalable.
  • Cost-Effective: You pay only for the compute time you consume.
  • Reduced Human Error: Automating the Terraform execution process minimizes the chances of manual errors.

Security Considerations

  • Ensure the Lambda function has minimal and necessary permissions (principle of least privilege).
  • Secure your S3 buckets to prevent unauthorized access to your Terraform files.


Integrating AWS Lambda with Terraform offers a powerful way to manage your cloud infrastructure. By automating Terraform plan applications, you can achieve more reliable, efficient, and error-free infrastructure deployments. This Python script is a step towards embracing the future of cloud infrastructure management, where automation is key.

Would you be interested in more content like this? Stay tuned to our blog ( for the latest in cloud computing and automation strategies.

· 3 min read
Saurav Gopinath Panda

CI/CD stands for Continuous Integration/Continous Delivery and is called CI/CD pipeline. It is an engineering practice that aims to automate and streamline the process of integrating code changes with Git repositories, testing them and delivering them to production systematically. It consists of various stages and processes that facilitate rapid, reliable, and consistent software delivery.

Critical Components of CI/CD Pipeline

Continuous Integration:

In Continuous Integration, the code checks and integrates the new code with the existing code. It builds, tests and merges the code, ensuring it is functional and clears all the test conditions.

Continuous Delivery

Continuous Delivery means that the code pushed by developers is continuously tested and merged into the branches, ensuring changes are product-ready.

Continuous Deployment:

Continuous Deployment pushes the code to production, where it is made readily available to the customer or QA team, depending on the environment. This automates manually logging in to a server, pulling the updated code, and making it live.

Why the CI/CD pipeline matters

The CI/CD pipeline is pivotal in modern software development methodologies like Agile and DevOps. Here’s how

Accelerated Development and Release Cycles

CI/CD enables rapid integration, testing, and code delivery, reducing development lifecycles and time-to-market for new features and improvements. Developers can quickly respond to changes in requirements or market demands, adapting and deploying updates efficiently.

Improved Code Quality

Continuous integration catches bugs and integration issues early in development, making them easier and cheaper to fix. Automated testing and validation maintain high software quality.

Risk Reduction and Error Prevention

Automated deployment processes reduce the risk of human error associated with manual deployments, leading to more reliable and consistent deployments. It provides immediate feedback from automated tests and helps identify issues early, preventing potential errors from reaching production.

Enhanced Team Productivity and Efficiency

CI/CD promotes collaboration and communication among development, testing, and operations teams, fostering a culture of continuous improvement and shared responsibility. By automating repetitive tasks, developers can focus more on value-adding activities, driving innovation and creativity.

When should you embrace CI/CD?

Setting up a CI/CD pipeline may take some time, but it is worth it as it helps establish stable processes. These processes can assist teams in setting up basic building blocks and encourage them to build tests, which are crucial while deploying at scale. Early adoption of CI/CD can help teams save significant effort and time in the future, especially when the systems start to scale, and manual deployments can be avoided.

Are you looking for ways to optimize your software development process? At Cloud Code AI, we’re utilizing AI assistants to assist teams in setting up efficient CI/CD pipelines in just a few minutes. By streamlining the development process, we help teams build and scale faster. If you’re interested in learning more about how AI can benefit your software development teams, sign up at