Astronomer guide: schedule Airflow DAGs on asset updates

Astronomer describes Assets as the mechanism that makes Airflow data-aware: DAGs sharing data declare relationships, and consumer DAGs schedule when assets update. The cleaned-table versus model-training example shows an ML DAG running only after the data engineering DAG updates the asset. Assets differ from object-storage file I/O features.

Cross-team data and AI groups gain an explicit handoff: producers publish asset updates; consumers bind schedules to those updates. That reduces brittle time coupling and clarifies when downstream work should run. The guide also points to viewing dependencies in the UI and to advanced scheduling options.

Named assets act as shared references to meaning—tables, files, models, or process milestones—that multiple systems and teams can agree on. Scheduling on those references is a practical form of governed interoperability between pipelines.