Apache Airflow
A platform to programmatically author, schedule, and monitor workflows as code.
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Programmatically author, schedule, and monitor workflows as code.
Core Features
- Define workflows as Python code (DAGs) with dependencies
- Robust scheduler with retries and backfill
- Rich web UI for monitoring and troubleshooting
- Extensible via plugins and operators
What It Can't Do
- •Default SQLite is for testing only; switch to PostgreSQL/MySQL for production. Requires Python >= 3.8. Steeper learning curve for beginners; scheduler process must run continuously. Resource consumption grows with DAG complexity.
Use Cases
- Data ETL pipelines and data warehouse integration
- Machine learning model training and deployment workflows
- Automated operational tasks and cron job replacement
Apache Airflow is an open-source workflow management platform that enables you to author, schedule, and monitor workflows programmatically. By defining workflows as Python code (DAGs), they become more maintainable, versionable, testable, and collaborative. The built-in scheduler executes tasks on a cluster of workers respecting dependencies, while the rich UI provides real-time pipeline visualization, progress tracking, and troubleshooting. Airflow is highly extensible with plugins and widely used for ETL, ML pipelines, and automation.
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Getting Started
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Step 1: Install Python 3.8+ and run pip install apache-airflow
Step 2: Initialize database: airflow db init
Step 3: Create admin user: airflow users create ...
- Step 1: Install Python 3.8+ and run pip install apache-airflow
- Step 2: Initialize database: airflow db init
- Step 3: Create admin user: airflow users create ...
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View GitHub SourceUninstall Info
Run pip uninstall apache-airflow, then manually remove the database file (default ~/airflow/airflow.db) and config file (airflow.cfg).
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