Mastering Task Dependencies in Airflow DAGs ??

Mastering Task Dependencies in Airflow DAGs ??

One of the most powerful features of Apache Airflow is how it handles task dependencies. Understanding upstream, downstream, parallel execution, and passing lists is crucial to designing efficient and reliable DAGs. Let’s break it down!

?? Upstream & Downstream Dependencies

• Upstream: A task that must run before another task.
• Downstream: A task that must run after another task.
• Example:

task_a >> task_b  # task_b runs after task_a (downstream)
task_c << task_a  # task_c runs after task_a (downstream)        

? Parallel Execution

Airflow allows parallel task execution when tasks don’t depend on each other.

task_a >> [task_b, task_c] # Both tasks run at the same time        

This speeds up workflows by avoiding unnecessary bottlenecks. ??

?? Passing Lists Between Tasks

Need to pass multiple values to a task? Use XComs to share data:

task_a = PythonOperator( task_id="generate_list", python_callable=lambda: ["item1", "item2", "item3"], ) task_b = PythonOperator( task_id="process_item", python_callable=process_data, op_args=["{{ ti.xcom_pull(task_ids='generate_list') }}"] )        

This allows each downstream task to receive and process individual items dynamically.

?? Why It Matters

Defining dependencies properly helps you avoid failures, optimize performance, and scale workflows efficiently. Whether it’s sequential, parallel, or dynamic task execution, Airflow gives you the flexibility to build robust pipelines.

What’s your go-to dependency pattern? Let’s discuss! ??

#ApacheAirflow #DataEngineering #ETL #WorkflowAutomation #Python