Apache Airflow: Data Pipeline Orchestration Basics

Learn Apache Airflow fundamentals—DAGs, scheduling, operators, and deployment patterns—so you can orchestrate reliable data pipelines with clear monitoring and retries.

Apache Airflow is the industry standard for orchestrating complex data workflows. This guide will get you started.

What is Apache Airflow?

Airflow is a platform to programmatically author, schedule, and monitor workflows as Directed Acyclic Graphs (DAGs).

Core Concepts

DAGs and Tasks

from airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime

with DAG(
    'my_data_pipeline',
    start_date=datetime(2024, 1, 1),
    schedule_interval='@daily',
    catchup=False
) as dag:

    extract = PythonOperator(
        task_id='extract',
        python_callable=extract_data
    )

    transform = PythonOperator(
        task_id='transform',
        python_callable=transform_data
    )

    load = PythonOperator(
        task_id='load',
        python_callable=load_data
    )

    extract >> transform >> load

Task Dependencies

PatternSyntaxUse Case
Sequentiala >> b >> cLinear flow
Parallel[a, b] >> cFan-in
BranchingBranchOperatorConditional

Best Practices

1. Idempotent Tasks

Tasks should produce the same result when run multiple times:

def load_data(ds, **context):
    # Delete existing data for this date
    delete_partition(ds)
    # Then load new data
    insert_data(ds)

2. Atomic Operations

Each task should be self-contained. For data validation, see Data Quality Testing: Ensuring Trust in Your Data.

3. Proper Monitoring

Integrate with your monitoring stack. See Monitoring Data Pipelines: Observability Best Practices for details.

Integrating with Python Pipelines

Use Airflow to orchestrate your Building Data Pipelines with Python: A Complete Guide:

from airflow.operators.python import PythonVirtualenvOperator

run_pipeline = PythonVirtualenvOperator(
    task_id='run_etl',
    python_callable=run_my_pipeline,
    requirements=['pandas', 'sqlalchemy']
)

Conclusion

Airflow provides powerful orchestration capabilities. Start simple and add complexity as your needs grow.


Orchestrate with confidence.