This repository provides a complete and ready-to-run Apache Airflow learning environment.

Key features:

• Ready-to-run Airflow setup powered by Docker and Astronomer CLI.
• Includes practical DAG examples.
• Provides a detailed documentation set under docs folder, explaining Airflow concepts, operators, and advanced usage.
• Demonstrates real-world integrations such as Slack notification and Postgres connections.

AIRFLOW-TRAINING/
├── dags/                # All DAGs (organized by category)
├── include/             # Shared Python modules (utils)
├── docs/                # Markdown documentation and guides
├── .astro/              # Astronomer CLI configuration
├── Dockerfile           # Custom Airflow image definition
├── requirements.txt     # Python dependencies
├── packages.txt         # Optional Linux packages
├── docker-compose.yaml  # Docker Compose setup for Airflow
└── README.md            # This documentation

You need the following installed before starting:

• Docker: Get Docker
• Astronomer CLI: Install Astronomer CLI

Ensure that the Docker daemon is running before starting any Airflow services.

astro dev start

astro dev stop

astro dev restart

• Default Airflow UI: http://localhost:8080

• Variables can be set in the Web UI: Admin → Variables

• To use external providers, add to requirements.txt:

  apache-airflow-providers-postgres
  apache-airflow-providers-slack
  

For detailed usage and configuration, refer to the official documentation: 👉 Apache Airflow User Guide 👉 Astro CLI docs

This project is inspired by courses from:

• Instructor: Marc Lamberti
• Platform: Udemy