Apache Airflow Orchestration

About

Master the art of workflow automation with this comprehensive guide to Apache Airflow orchestration. This skill provides deep technical guidance on authoring, scheduling, and monitoring Directed Acyclic Graphs (DAGs), enabling you to build scalable ETL/ELT pipelines with complex task dependencies. From utilizing core operators and sensors to implementing advanced patterns like dynamic workflow generation, TaskGroups for organization, and cross-DAG communication via XComs, this resource ensures your data engineering platforms are production-ready, observable, and maintainable.

Key Features

• Advanced task management using TaskGroups, edge labeling, and conditional branching logic.
• Best practices for inter-task communication and state management using XComs and context variables.
• Comprehensive DAG development patterns including linear, fan-in/out, and dynamic chaining.
• 17 GitHub stars
• Implementation guidance for specialized sensors like ExternalTaskSensor and FileSensor for event-driven flows.
• Production deployment strategies covering Local, Celery, and Kubernetes executors.

Use Cases

• Automating large-scale batch processing jobs with sophisticated retry logic and scheduling patterns.
• Building and monitoring complex ETL/ELT data pipelines across heterogeneous systems.
• Implementing event-driven workflows that trigger based on external task completion or file arrival.