A Directed Acyclic Graph (DAG) is a mathematical abstraction used to represent data pipelines, depicting a series of tasks executed in a specific order without self-referencing loops, thereby ensuring efficient data processing. DAGs consist of nodes, which represent tasks, and directed edges that dictate the sequence of execution, ensuring that data flows in one direction and preventing infinite loops. This structure is particularly useful for visualizing workflows and identifying areas for optimization. Code-based pipeline tools like Apache Airflow use DAGs to enhance productivity by automating data workflows, offering dynamic, extensible, and manageable solutions that integrate seamlessly into developer workflows. In Airflow, DAGs represent data pipelines where tasks, defined by operators, are executed in a specified sequence, allowing for reliable data orchestration and clear visualization of dependencies. By using DAGs, organizations can improve data pipeline management, ensuring accurate data processing and reporting by integrating with various systems and providing alerts in case of errors.