Can you explain what a DAG in Airflow is?

A DAG, or Directed Acyclic Graph, is the fundamental unit of scheduling in Airflow. It is a Python object that represents a collection of tasks and their dependencies. Each task is an instance of an Operator (e.g., BashOperator, PythonOperator). The DAG defines the execution order by specifying which tasks must run before others, ensuring there are no cycles (hence acyclic). It also contains metadata such as the schedule_interval, start_date, default_args, and tags. When Airflow’s scheduler runs, it looks at the DAG definition, determines which tasks are ready to run based on dependencies and timing, and dispatches them to the executor. The DAG file is typically a simple Python script that imports the DAG class, sets default arguments, creates a DAG instance, and defines tasks and their relationships.