In Airflow, a DAG (Directed Acyclic Graph) is a collection of tasks that you want to run, organized in a way that reflects their relationships and dependencies. DAGs are the core concept of Airflow, and they specify how tasks should run and what their dependencies are. DAGs are defined in Python code and are organized to show relationships between tasks in the Airflow UI. The mathematical properties of DAGs make them useful for building data pipelines. In Airflow, every single operator/task must be assigned to a DAG in order to run. 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.