This post is intended to assist users in understanding and replicating a method to unit test Python-based ETL Glue Jobs, using the PyTest Framework in AWS CodePipeline. In the current practice, several options exist for unit testing Python scripts for Glue jobs in a local environment. Although a local development environment may be set up to build and unit test Python-based Glue jobs, by following the documentation, replicating the same procedure in a DevOps pipeline is difficult and time consuming.

Unit test scripts are one of the initial quality gates used by developers to provide a high-quality build. One must reuse these scripts during regression testing to make sure that all of the existing functionality is intact, and that new releases don’t disrupt key application functionality. The majority of the regression test suites are expected to be integrated with the DevOps Pipeline for its execution. Unit testing an application code is a fundamental task that evaluates  whether each (unit) code written by a programmer functions as expected. Unit testing of code provides a mechanism to determine that software quality hasn’t been compromised. One of the difficulties in building Python-based Glue ETL tasks is their ability for unit testing to be incorporated within DevOps Pipeline, especially when there are modernization of mainframe ETL process to modern tech stacks in AWS

AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, machine learning (ML), and application development. AWS Glue provides all of the capabilities needed for data integration. This means that you can start analyzing your data and putting it to use in minutes rather than months. AWS Glue provides both visual and code-based interfaces to make data integration easier.


GitHub Repository

Amazon ECR Image URI for Glue Library

Solution overview

A typical enterprise-scale DevOps pipeline is illustrated in the following diagram. This solution describes how to incorporate the unit testing of Python-based AWS Glue ETL processes into the AWS DevOps Pipeline.

Figure 1 Solution Overview

The GitHub repository aws-glue-jobs-unit-testing has a sample Python-based Glue job in the src folder. Its associated unit test cases built using the Pytest Framework are accessible in the tests folder. An AWS CloudFormation template written in YAML is included in the deploy folder. As a runtime environment, AWS CodeBuild utilizes custom container images. This feature is used to build a project utilizing Glue libraries from Public ECR repository, that can run the code package to demonstrate unit testing integration.

Solution walkthrough

Time to read  7 min
Time to complete  15-20 min
Learning level  300
Services used
AWS CodePipeline, AWS CodeCommit, AWS CodeBuild, Amazon Elastic Container Registry (Amazon ECR) Public Repositories, AWS CloudFormation

The container image at the Public ECR repository for AWS Glue libraries includes all of the binaries required to run PySpark-based AWS Glue ETL tasks locally, as well as unit test them. The public container repository has three image tags, one for each AWS Glue version supported by AWS Glue. To demonstrate the solution, we use the image tag glue_libs_3.0.0_image_01 in this post. To utilize this container image as a runtime image in CodeBuild, copy the Image URI corresponding to the image tag that you intend to use, as shown in the following image.

Figure 2 Select Glue Library from Public ECR

The aws-glue-jobs-unit-testing GitHub repository contains a CloudFormation template, pipeline.yml, which deploys a CodePipeline with CodeBuild projects to create, test, and publish the AWS Glue job. As illustrated in the following, use the copied image URL from Amazon ECR public to create and test a CodeBuild project.

 TestBuild: Type: AWS::CodeBuild::Project Properties: Artifacts: Type: CODEPIPELINE BadgeEnabled: false Environment: ComputeType: BUILD_GENERAL1_LARGE Image: "" ImagePullCredentialsType: CODEBUILD PrivilegedMode: false Type: LINUX_CONTAINER Name: !Sub "${RepositoryName}-${BranchName}-build" ServiceRole: !GetAtt CodeBuildRole.Arn 

The pipeline performs the following operations:

  1. It uses the CodeCommit repository as the source and transfers the most recent code from the main branch to the CodeBuild project for further processing.
  2. The following stage is build and test, in which the most recent code from the previous phase is unit tested and the test report is published to CodeBuild report groups.
  3. If all of the test results are good, then the next CodeBuild project is launched to publish the code to an Amazon Simple Storage Service (Amazon S3) bucket.
  4. Following the successful completion of the publish phase, the final step is to deploy the AWS Glue task using the CloudFormation template in the deploy folder.

Deploying the solution

Set up

Now we’ll deploy the solution using a CloudFormation template.

  • Using the GitHub Web, download the file from the aws-glue-jobs-unit-testing repository. This zip file contains the GitHub repository’s src, tests, and deploy folders. You may also create the zip file yourself using command-line tools, such as git and zip. To create the zip file on Linux or Mac, open the terminal and enter the following commands.
git clone
cd aws-glue-jobs-unit-testing
git checkout master
zip -r src/ tests/ deploy/
  • Sign in to the AWS Management Console and choose the AWS Region of your choice.
  • Create an Amazon S3 bucket. For more information, see How Do I Create an S3 Bucket? in the AWS documentation.
  • Upload the downloaded zip package,, to the Amazon S3 bucket that you created.

In this example, I created an Amazon S3 bucket named aws-glue-artifacts-us-east-1 in the N. Virginia (us-east-1) Region, and used the console to upload the zip package from the GitHub repository to the Amazon S3 bucket.

Figure 3 Upload file to S3 bucket

Creating the stack

  1.  In the CloudFormation console, choose Create stack.
  2. On the Specify template page, choose Upload a template file, and then choose the pipeline.yml template, downloaded from the GitHub repository

Figure 4 Upload pipeline.yml template to create a new CloudFormation stack

  1. Specify the following parameters:.
  • Stack name: glue-unit-testing-pipeline (Choose a stack name of your choice)
  • ApplicationStackName: glue-codepipeline-app (This is the name of the CloudFormation stack that will be created by the pipeline)
  • BranchName: master (This is the name of the branch to be created in the CodeCommit repository to check-in the code from the Amazon S3 bucket zip file)
  • BucketName: aws-glue-artifacts-us-east-1 (This is the name of the Amazon S3 bucket that contains the zip file. This bucket will also be used by the pipeline for storing code artifacts)
  • CodeZipFile: (This is the key name of the sample code Amazon S3 object. The object should be a zip file)
  • RepositoryName: aws-glue-unit-testing (This is the name of the CodeCommit repository that will be created by the stack)
  • TestReportGroupName: glue-unittest-report (This is the name of the CodeBuild test report group that will be created to store the unit test reports)

Figure 5 Fill parameters for stack creation

  1. Choose Next, and again Next.
  1. On the Review page, under Capabilities, choose the following options:
  • I acknowledge that CloudFormation might create IAM resources with custom names.

Figure 6 Acknowledge IAM roles creation

  1. Choose Create stack to begin the stack creation process. Once the stack creation is complete, the resources that were created are displayed on the Resources tab. The stack creation takes approximately 5-7 minutes.

Figure 7 Successful completion of stack creation

The stack automatically creates a CodeCommit repository with the initial code checked-in from the zip file uploaded to the Amazon S3 bucket. Furthermore, it creates a CodePipeline view using the CodeCommit repository as the source. In the above example, the CodeCommit repository is aws-glue-unit-test, and the pipeline is aws-glue-unit-test-pipeline.

Testing the solution

To test the deployed pipeline, open the CodePipeline console and select the pipeline created by the CloudFormation stack. Select the Release Change button on the pipeline page.

Figure 8 Choose Release Change on pipeline page

The pipeline begins its execution with the most recent code in the CodeCommit repository.

When the Test_and_Build phase is finished, select the Details link to examine the execution logs.

Figure 9 Successfully completed the Test_and_Build stage

Select the Reports tab, and choose the test report from Report history to view the unit execution results.

Figure 10 Test report from pipeline execution

Finally, after the deployment stage is complete, you can see, run, and monitor the deployed AWS Glue job on the AWS Glue console page. For more information, refer to the Running and monitoring AWS Glue documentation

Figure 11 Successful pipeline execution


To avoid additional infrastructure costs, make sure that you delete the stack after experimenting with the examples provided in the post. On the CloudFormation console, select the stack that you created, and then choose Delete. This will delete all of the resources that it created, including CodeCommit repositories, IAM roles/policies, and CodeBuild projects.


In this post, we demonstrated how to unit test and deploy Python-based AWS Glue jobs in a pipeline with unit tests written with the PyTest framework. The approach is not limited to CodePipeline, and it can be used to build up a local development environment, as demonstrated in the Big Data blog. The aws-glue-jobs-unit-testing GitHub repository contains the example’s CloudFormation template, as well as sample AWS Glue Python code and Pytest code used in this post. If you have any questions or comments regarding this example, please open an issue or submit a pull request.


Praveen Kumar Jeyarajan

Praveen Kumar Jeyarajan is a PraveenKumar is a Senior DevOps Consultant in AWS supporting Enterprise customers and their journey to the cloud. He has 11+ years of DevOps experience and is skilled in solving myriad technical challenges using the latest technologies. He holds a Masters degree in Software Engineering. Outside of work, he enjoys watching movies and playing tennis.

Vaidyanathan Ganesa Sankaran

Vaidyanathan Ganesa Sankaran is a Sr Modernization Architect at AWS supporting Global Enterprise customers on their journey towards modernization. He is specialized in Artificial intelligence, legacy Modernization and Cloud Computing. He holds a Masters degree in Software Engineering and has 12+ years of Modernization experience. Outside work, he loves conducting training sessions for college grads and professional starter who wants to learn cloud and AI. His hobbies are playing tennis, philately and traveling.


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