Can you tell me about DAGs in Airflow?

In Apache Airflow, a DAG (Directed Acyclic Graph) is the core abstraction that defines a workflow. It is a Python object that lists tasks and their dependencies. Each task is an operator (e.g., BashOperator, PythonOperator). The DAG specifies the schedule interval, start date, and default arguments. Airflow’s scheduler parses the DAG, builds a dependency graph, and pushes tasks to the executor. Because the graph is acyclic, tasks can be executed in parallel where dependencies allow. DAGs enable versioning, retries, and monitoring via the Airflow UI. They are ideal for ETL pipelines, data processing, and any repeatable workflow.