How to Configure the GlueJobOperator in Apache Airflow

Data engineering often requires setting up workflows that seamlessly connect multiple tools. One common challenge is integrating Apache Airflow with AWS Glue to create, manage, and schedule ETL (Extract, Transform, Load) jobs. In this guide, I’ll walk you through the steps to configure the GlueJobOperator in Airflow and share practical tips to avoid common pitfalls.

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Step 1: Create Your Glue Job

AWS Glue provides two ways to define jobs:

1. Visual ETL: A drag-and-drop interface ideal for simpler use cases.
2. Custom Python Scripts: Offers flexibility for complex transformations.

For simplicity, let’s assume you’re using Visual ETL to extract data from S3, transform it, and save it back to S3. Save your job script in a clearly defined S3 path for later reference, such as s3://enabledata/scripts/.

Step 2: Configure AWS Permissions

Setting up permissions can be tricky. Here’s a quick breakdown:

For Glue

• Create a role for Glue with the necessary permissions:
• AWS-managed Glue service role: AWSGlueServiceRole.