Airflow Core Concepts and Architecture
Airflow Core Concepts and Architecture
Apache Airflow stands as the industry-standard orchestration platform for building and managing complex data pipelines. Its architecture is fundamentally designed around declarative workflow definition, allowing teams to visualize and automate tasks with clarity. At the heart of Airflow lies the DAG (Directed Acyclic Graph), which serves as the blueprint for your entire workflow. By defining dependencies between tasks, DAGs ensure data flows logically without cycles, enabling reliable execution sequences.
Beyond the DAG definition, Airflow leverages modular components to handle execution and scheduling:
- Operator: Represents the actual execution unit of a task, supporting a wide range of operations from simple scripts to complex Python functions.
- Executor: Manages how tasks are dispatched to the underlying workers, offering flexibility between centralized control and distributed processing.
- Scheduler: Continuously monitors the state of DAGs and triggers tasks when their dependencies are met.
Together, these elements create a robust system where tasks are automatically scheduled, retried on failure, and monitored in real time. Whether you are orchestrating daily ETL jobs or orchestrating multi-day batch processes, Airflow provides the structure needed to maintain consistency and reliability. Mastering these core concepts will equip you to design scalable, production-ready pipelines that evolve alongside your data needs.
Airflow Core Concepts and Architecture
- Airflow Connection Introduction
- Airflow Overview
- DAG Introduction
- Airflow Operator Introduction
- Airflow Concepts and Components
- Defining Airflow DAGs
- DAG Dependencies
- DAG Scheduling Strategies
- DAG Best Practices
- Introduction to the Apache Airflow Command Line Interface (CLI)
- Airflow Web UI Basics
- Introduction to Airflow Hooks
- Integrating Apache Airflow with CI/CD Pipelines