What is apache airflow ?

Apache Airflow is an open-source platform designed to programmatically author, schedule, and monitor workflows. It was created to help automate complex processes and workflows by defining them as Directed Acyclic Graphs (DAGs). A DAG is a series of tasks that are arranged in a specific order, allowing for dependencies between tasks and a clear flow of execution.

Key features and concepts of Apache Airflow

Directed Acyclic Graphs (DAGs): Workflows in Airflow are represented as DAGs, which are composed of tasks with dependencies. The DAGs define how tasks are scheduled and executed in a specified order.

• Tasks and Operators: Each task in a DAG is executed using an operator. Operators are abstractions that define the operations to be performed, such as running a Python script (PythonOperator), executing a shell command (BashOperator), or querying a database.

• Scheduling: Airflow allows for scheduling workflows to run at specific times or intervals using scheduling expressions similar to cron syntax.

• Monitoring: Airflow provides a web-based user interface for monitoring and managing workflows, including visualizing the DAGs, tracking task progress, and troubleshooting issues.

• Hooks and Connections: Airflow provides hooks to interact with various external services and databases, making it easy to integrate workflows with other systems. Connections are configurations used to establish connections to these external systems.

• Executors: Airflow supports different execution backends (executors) for running tasks, such as the LocalExecutor, CeleryExecutor (for distributed execution), and KubernetesExecutor (for Kubernetes integration).

• Extensibility: Airflow is highly extensible, allowing users to create custom operators, hooks, and plugins to meet specific needs.