Apache Airflow

About Apache Airflow

Platform to programmatically author, schedule, and monitor workflows. Allows for complex pipeline construction and efficient task management with robust dependency handling.

Key Features

• 1DAG-based workflow definition using pure Python code
• 2Rich web UI for monitoring, triggering, and debugging pipeline runs
• 3Extensible operator library covering databases, cloud services, HTTP, and more
• 4Built-in retry logic, SLA tracking, and alerting on task failure
• 5Large open-source ecosystem with 1000+ community-contributed providers

How Python Data Engineers Use Apache Airflow

Python data engineers define pipelines as Directed Acyclic Graphs (DAGs) using Airflow's Python SDK. DAGs are written as .py files that instantiate Operators — PythonOperator for custom logic, BashOperator for shell commands, and provider-specific operators for Postgres, S3, BigQuery, and Snowflake. Airflow is the industry-standard orchestrator for scheduling ETL jobs, managing dependencies between tasks, and handling retries in production data pipelines.