The way to Automate PySpark Pipelines on AWS EMR With Airflow | by Antonello Benedetto | Aug, 2023

Within the dynamic panorama of knowledge engineering and analytics, constructing scalable and automatic pipelines is paramount.

Spark lovers who’ve been working with Airflow for some time could be questioning:

The way to execute a Spark job on a distant cluster utilizing Airflow?

The way to automate Spark pipelines with AWS EMR and Airflow?

On this tutorial we’re going to combine these two applied sciences by exhibiting methods to:

1. Configure and fetch important parameters from the Airflow UI.
2. Create auxiliary features to mechanically generate the popular spark-submit command.
3. Use Airflow’s EmrAddStepsOperator() methodology to construct a job that submits and executes a PySpark job to EMR
4. Use Airflow’s EmrStepSensor() methodology to watch the script execution.

The code used on this tutorial is accessible on GitHub.

• An AWS account with a S3 bucket and EMR cluster configured on the identical area ( on this case eu-north-1). The EMR cluster needs to be accessible and in WAITING state. In our case it has been named emr-cluster-tutorial:

• Some mock balances information already accessible within the S3 bucket below the src/balances folder. Knowledge might be generated and written to the situation utilizing the data producer script.
• The required JARs ought to already downloaded from Maven and accessible within the S3 bucket.
• Docker put in and working on the native machine with 4-6 GB of allotted reminiscence.

The purpose is to jot down some mock information in parquet format to a S3a bucket after which construct a DAG that:

• Fetches required configuration from Airflow UI;
• Uploads a pyspark script to the identical S3a bucket;