Apache Airflow 2.4 introduces several significant enhancements, with the standout feature being data-driven scheduling enabled by the new Dataset class, which allows for more granular control over task dependencies and the automatic triggering of downstream DAGs based on the successful completion of upstream tasks. This advancement facilitates the breakdown of large, monolithic DAGs into smaller, manageable units, enhancing performance and simplifying maintenance. Additionally, Airflow 2.4 consolidates scheduling parameters into a single schedule parameter, expands dynamic task mapping capabilities, and offers UI improvements for easier navigation and log access. The release also phases out smart sensors in favor of more flexible deferrable operators, promoting asynchronous event-driven operations. While the new datasets feature currently cannot span across separate Airflow deployments, it represents a significant leap in enabling organizations to optimize data pipeline operations and improve data governance.