When would you choose Airflow over EKS?

When it comes to orchestrating complex workflows, Apache Airflow and Amazon EKS (Elastic Kubernetes Service) serve different purposes and are suited to different use cases. You would choose Airflow over EKS when your primary focus is on managing data workflows and pipelines efficiently rather than orchestrating containerized applications. Airflow is designed for scheduling and monitoring workflows, providing a user-friendly UI for tracking task progress, and enabling dependency management. In contrast, EKS is a fully managed Kubernetes service intended for deploying and managing containerized applications, which may include microservices, batch processing, and more.

For example, if you're tasked with processing large amounts of data in a sequence of dependent tasks (like data extraction, transformation, and loading), Airflow's Directed Acyclic Graph (DAG) capabilities are ideal. It allows you to define task dependencies clearly and makes it easier to add new tasks in the future.

# Sample Airflow DAG example
from airflow import DAG
from airflow.operators.dummy_operator import DummyOperator
from airflow.operators.python_operator import PythonOperator
from datetime import datetime

def my_task():
    print("Executing Task")

dag = DAG('my_dag', start_date=datetime(2021, 1, 1), schedule_interval='@daily')

start = DummyOperator(task_id='start', dag=dag)
task1 = PythonOperator(task_id='my_task', python_callable=my_task, dag=dag)
end = DummyOperator(task_id='end', dag=dag)

start >> task1 >> end