Run unit tests for python ETL jobs in AWS Glue using the pytest framework

Know more about unit testing AWS Glue ETL with pytest :

This template provides a CI/CD pipeline that automates unit testing for AWS Glue Python ETL jobs with the Pytest framework. The pipeline engages major AWS services including CodePipeline, CodeBuild, CodeCommit, and CloudFormation to handle the whole process from code storage to test execution and the deployment of error-free ETL jobs. It guarantees the accuracy, testing, and production readiness of data transformations.

Why This Template Transforms Data Team Operations ?

This template gives a method that is automatic and systematic for a process that is often manual and error-prone. Merging unit testing with Pytest into a completely automated pipeline makes it possible for the teams to discover the defects in the code during their first stage, improve the quality of the code, and lessen the incidents in the production environment. The result is an unreliable, non-scalable, and inefficient testing workflow for cloud-based data pipelines.

Who needs this template, and when is the best time to use it ?

This template is perfect for data engineers, Python developers, and cloud architects who work with AWS Glue. It is particularly for managing complex ETL workflows or scaling data platforms. Teams that are preparing regular deployments or looking to shift to a DevOps model for data pipelines will benefit greatly from adopting this automated testing framework.
What features does it offer?

• AWS CodePipeline – Manages the overall CI/CD pipeline for ETL testing and deployment

• AWS CodeCommit Repository – Stores Python ETL scripts and test code

• AWS CodeBuild – Runs unit tests using the Pytest framework

• Amazon ECR – Stores containerized libraries required for Glue jobs

• AWS Glue ETL Job – Executes the actual data transformation logic

• AWS CloudFormation – Automates the infrastructure needed for testing and deployment

• IAM Roles – Manages secure access for pipeline components

• Source Stage – Handles version control and source management.

• Build Stage – Executes testing scripts and generates test results.s

• Deployment Stage – Deploys validated jobs into AWS Glue.

• CloudWatch Logs – Tracks test outputs and errors

• Lambda Functions – Automates post-test triggers for workflows

• S3 Buckets – Stores logs and testing results

• Notification System – Sends alerts when test cases fail.

How to get started with Cloudairy ?

Set up your workflow easily with a few simple steps in Cloudairy :

• Log in to your Cloudairy account and navigate to the Templates section.
• Search for “AWS Glue ETL Unit Testing Workflow” and open the template.
• Review its pre-configured components, customize it based on your test scripts, and configure the pipeline stages.
• Save your setup or deploy it directly into your environment for automated testing and deployment.

Summary

With this template, it becomes a lot simpler to execute unit tests for AWS Glue Python ETL jobs utilizing Pytest and at the same time, the integration with a CI/CD pipeline is smooth. Through the utilization of tools such as CodePipeline, CodeBuild, and CloudFormation, one can increase automation, enhance accuracy, and make data workflows faster. It is a fantastic choice for organizations that wish to build more trustworthy, test-driven, and production-ready cloud data pipelines.