What is Apache Airflow?

Airflow is an open-source platform for programmatically authoring, scheduling, and monitoring workflows. It allows defining tasks and their dependencies in Python code, visualizing their graph (DAG), running tasks on a schedule or triggers, and tracking their execution via a web interface.

Main components of Airflow:

• Scheduler: Launches scheduled workflows.
• Webserver: Provides a user interface for management and monitoring.
• Worker(s): Execute tasks (depends on the Executor).
• Database: Stores the state of workflows, tasks, configuration, and history.

DAG (Directed Acyclic Graph) is the core concept of Airflow. A DAG is a set of tasks (operators) connected by dependencies in the form of a directed graph without cycles.

Examples of operators:

• BashOperator: Executes a bash command.
• PythonOperator: Executes a Python function.
• PostgresOperator: Executes an SQL query in PostgreSQL.

Distributed architecture allows scaling Airflow using various executors:

• SequentialExecutor: For testing, executes tasks sequentially.
• LocalExecutor: Runs tasks in parallel on a single machine.
• CeleryExecutor: Uses Celery for distributed task execution across multiple workers.
• KubernetesExecutor: Runs each task in a separate Kubernetes pod.

Advantages of Airflow:

• Programmatic creation: Workflows are defined in Python code.
• Dynamism: DAGs can be generated dynamically.
• Extensibility: Easily create custom operators and hooks.
• Scalability: Architecture allows system scaling.
• User interface: Convenient web interface for monitoring and management.