Data pipelines don't just 'happen'. They need a manager to handle failures, retries, and timing. Apache Airflow is the industry standard for programmatic orchestration.
1The Directed Acyclic Graph
A DAG is a visual and logical representation of your workflow. Directed means the flow moves in one direction. Acyclic means there are no loops (Task A can't depend on Task B if Task B depends on Task A). This structure ensures that Airflow always knows exactly what to run next and can pinpoint exactly where a failure occurred if a pipeline breaks.
DAG_Logic:
Step_1: [FETCH_DATA]
Step_2: [CLEAN_DATA] depends_on Step_1
Step_3: [TRAIN_MODEL] depends_on Step_2
Status: ORCHESTRATION_DEFINED2The Control Plane
Airflow consists of several components: the Web Server (the UI), the Scheduler (the brain that decides when to run tasks), and Workers (the muscle that executes the code). Because Airflow is written in Python, you can use any Python library within your tasks, making it incredibly flexible for everything from SQL transformations to calling LLM APIs.
from airflow import DAG
from airflow.operators.python import PythonOperator
with DAG('daily_ai_update', schedule='@daily') as dag:
t1 = PythonOperator(task_id='ingest', python_callable=fetch_func)
t2 = PythonOperator(task_id='train', python_callable=train_func)
t1 >> t2 # Set dependency