Airflow Cookbook

Welcome to the Apache Airflow Cookbook! This section provides practical examples and recipes for common Airflow use cases, helping you build robust and efficient data pipelines.

Getting Started

Before diving into specific recipes, ensure you have a basic understanding of Airflow concepts like DAGs, Operators, Tasks, and Connections.

Recipes

• Triggering a DAG Run

  Learn how to programmatically trigger other DAGs or specific DAG runs using Airflow's API and operators.

• Managing Connections

  Discover best practices for configuring and managing connections to various external services and databases.

• Creating Custom Operators

  Extend Airflow's functionality by building your own custom operators tailored to your specific needs.

• Generating DAGs Dynamically

  Explore techniques for creating DAGs on the fly based on external configurations or discovery.

• Passing Data Between Tasks (XComs)

  Understand how to use XComs to pass small amounts of metadata or results between tasks in a DAG.

• Working with Sensors

  Learn how to use sensors to wait for specific conditions to be met before proceeding with your pipeline.

• Advanced Scheduling Strategies

  Dive into more complex scheduling scenarios, including cron expressions, time deltas, and triggers.

• Monitoring and Alerting

  Set up effective monitoring and alerting mechanisms to keep track of your DAGs' health and performance.

Common Patterns

Here are some common patterns you'll encounter when building Airflow pipelines:

• ETL/ELT Pipelines: Extract, Transform, and Load data from various sources.
• Data Quality Checks: Implement automated checks to ensure data integrity.
• Machine Learning Workflows: Orchestrate model training, evaluation, and deployment.
• Reporting and Analytics: Automate the generation of reports and dashboards.

Contributing

Have a great recipe to share? We encourage you to contribute to the Airflow documentation! Please refer to our contributing guide for more details.