Integrating Glue with Apache Airflow on MWAA

As organizations deal with ever-growing volumes of data, automating data pipelines and orchestrating ETL workflows becomes crucial. AWS Glue is a fully managed ETL service designed for data integration, while Apache Airflow is a powerful orchestration tool used for workflow scheduling. Amazon Managed Workflows for Apache Airflow (MWAA) simplifies the deployment of Airflow on AWS. When you integrate Glue with Airflow on MWAA, you get the best of both: scalable ETL with Glue and flexible orchestration with Airflow.

In this blog, we’ll explore how to integrate AWS Glue with Apache Airflow on MWAA, and discuss best practices for managing ETL pipelines efficiently.


Why Integrate Glue with Airflow?

Before diving into implementation, let’s understand the benefits of this integration:

Orchestrate multiple ETL steps across Glue, EMR, Redshift, and S3.

Schedule and monitor workflows using Airflow’s rich UI and DAGs.

Build data pipelines that are modular, scalable, and easy to maintain.

Automate data quality checks, transformations, and loading across stages.


Prerequisites

To begin with, ensure the following are set up:

An AWS Glue job created and available.

An MWAA environment with the necessary permissions and configurations.

An IAM role attached to MWAA with access to Glue.

Apache Airflow 2.x with the AWS provider package installed (apache-airflow-providers-amazon).


Step-by-Step Integration

1. Create the Glue Job

Create your Glue ETL job in the AWS Glue console:

Choose a Spark or Python shell job.

Specify the source and target locations (S3, Redshift, etc.).

Save and note down the Job Name.


2. Configure MWAA Environment

Ensure that:

MWAA has access to the VPC, subnets, and security groups.

The execution role has the following policies:

AmazonMWAAFullConsoleAccess

AWSGlueConsoleFullAccess

AmazonS3FullAccess (for log and data storage)

Also, add the required environment variables and Python packages in MWAA settings.


3. Use AWS Glue Operator in Airflow DAG

Airflow provides the AwsGlueJobOperator to trigger Glue jobs from within DAGs.

Here’s an example DAG to run a Glue job:


python


from airflow import DAG

from airflow.providers.amazon.aws.operators.glue import AwsGlueJobOperator

from datetime import datetime


default_args = {

    'owner': 'airflow',

    'start_date': datetime(2023, 1, 1),

    'retries': 1

}


with DAG('glue_job_trigger',

         default_args=default_args,

         schedule_interval='@daily',

         catchup=False) as dag:


    glue_task = AwsGlueJobOperator(

        task_id='run_glue_etl',

        job_name='my_glue_job_name',

        script_location='s3://my-script-path/glue_script.py',

        region_name='us-east-1',

        iam_role_name='my-glue-role'

    )


    glue_task


4. Monitor in MWAA UI

Once deployed, you can:

Track job execution status in the Airflow UI.

View logs in CloudWatch (configured via MWAA).

Check Glue job history in the AWS Glue Console.


Best Practices

Modular DAGs: Break large workflows into smaller Glue jobs and orchestrate them in Airflow.

Logging and Alerts: Use CloudWatch alerts for job failures.

Retries and Timeouts: Configure appropriate retries and timeouts in your Airflow tasks.

Version Control: Store DAGs and Glue scripts in Git or S3 for traceability.

Security: Use fine-grained IAM roles and restrict access via VPC endpoints.


Conclusion

Integrating AWS Glue with Apache Airflow on MWAA offers a powerful solution for orchestrating data pipelines at scale. It combines the simplicity and scalability of Glue with the flexibility and control of Airflow. Whether you're building batch ETL pipelines or real-time workflows, this integration ensures better automation, monitoring, and performance in your data engineering projects.

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