In Apache Airflow, a DAG (Directed Acyclic Graph) is a data pipeline defined in Python code that represents a collection of tasks you want to run and is organized to show relationships between tasks in the Airflow UI. DAGs are the core concept of Airflow, collecting tasks together, organized with dependencies and relationships to say how they should run. DAGs are acyclic, meaning tasks cannot have a dependency to themselves, and directed, meaning if multiple tasks exist, then each task must have at least one defined upstream or downstream task. A DAG file in Airflow is a Python script that defines and organizes tasks in a workflow, specifying the order in which tasks should be executed and their dependencies. Every single operator/task must be assigned to a DAG in order to run, and there are several ways of calculating the DAG without passing it explicitly. If you want to see a visual representation of a DAG, you can load up the Airflow UI, navigate to your DAG, and select “Graph” or run airflow dags show, which renders it out as an image file.