Implementing version control for Glue jobs with Git

As data pipelines grow in complexity, maintaining code quality, traceability, and team collaboration becomes essential. For developers working with AWS Glue, version control is often overlooked but critical. By integrating Git with your Glue job scripts, you can bring structure, history tracking, and collaborative workflows to your data engineering projects.

In this blog, we’ll explore why version control is important for AWS Glue jobs, and how to implement it effectively using Git.


🚀 Why Use Git with AWS Glue?

AWS Glue is a serverless data integration service that lets you run ETL jobs using Python or Scala. By default, Glue stores job scripts in its internal script editor or in Amazon S3. However, without Git:

You can’t track who made changes or when

Rollbacks become difficult

Collaboration across teams is limited

Testing or staging environments are harder to manage

Using Git solves these problems by offering:

✅ Code history and change tracking

✅ Branching for safe experimentation

✅ Team collaboration via pull requests

✅ CI/CD integration possibilities


🧰 Project Structure for Glue + Git

Here’s a typical structure for organizing Glue scripts in a Git repo:


arduino

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Edit

/glue-jobs

  ├── job1/

  │   ├── script.py

  │   ├── config.json

  ├── job2/

  │   ├── script.py

  │   ├── utils.py

  └── shared/

      ├── libraries/

      └── transformations/

.gitignore

README.md

This structure separates job logic and shared utilities, making versioning cleaner and reuse easier.

🔧 Step-by-Step: Setting Up Git for Glue Jobs

1. Clone or Create a Git Repository

Use GitHub, GitLab, or Bitbucket to host your repository.

bash


git clone https://github.com/your-org/glue-jobs.git

cd glue-jobs

2. Write or Organize Glue Scripts Locally

Instead of writing code in the AWS Glue console, write and test your scripts locally using an IDE like VS Code or PyCharm. Store each job in its own folder with relevant dependencies.


3. Use .gitignore to Exclude Temporary Files

Create a .gitignore to avoid tracking unnecessary files:

bash

*.pyc

__pycache__/

.env

4. Push Scripts to Git Repository

bash


git add .

git commit -m "Initial commit of Glue jobs"

git push origin main

Now your Glue job scripts are version-controlled and ready for collaboration.


🔁 Deploy Scripts from Git to AWS Glue

There are several ways to deploy version-controlled scripts to AWS Glue:

A. Manual Upload to S3

Push your updated script to the Git repo

Upload the script manually or with a CLI to the S3 path used by your Glue job


bash


aws s3 cp glue-jobs/job1/script.py s3://your-bucket/glue-scripts/job1/script.py

B. Automated CI/CD with GitHub Actions or CodePipeline

Set up a pipeline that triggers on Git changes and updates the S3 bucket or directly modifies the Glue job using AWS SDK (boto3):


python

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import boto3

client = boto3.client('glue')

client.update_job(

    JobName='your-glue-job',

    JobUpdate={'Command': {'ScriptLocation': 's3://your-bucket/path/script.py'}}

)

✅ Best Practices

Use branches for development, staging, and production

Include a README in each job folder with job details

Use code reviews via pull requests

Store config files separately from scripts for flexibility


🏁 Final Thoughts

By implementing Git-based version control for your AWS Glue jobs, you make your ETL pipelines more maintainable, auditable, and scalable. Whether you're working solo or in a team, Git ensures every change is traceable, reversible, and deployable—bringing DevOps best practices into your data engineering workflow.

Ready to version your Glue jobs? Start by organizing your scripts and setting up your first Git repo today.



Learn AWS Data Engineer Training

Read More: Trigger-based data partitioning in S3

Read More: Enabling compression in Redshift COPY command
Read More: Writing custom job bookmarks in AWS Glue

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