State-aware orchestration has gained attention recently, particularly following the dbt Coalesce 2025 announcement. While many practitioners are excited about this concept, it's important to recognize that the idea of managing "state" isn’t new. Technologies like dlt, sqlmesh, and Spark have utilized state for years, contributing to efficient orchestration. Despite potential cost savings—reported at up to 64% for SQL queries—some data practitioners remain indifferent. A significant point of confusion is the distinction between state-aware orchestration and Fusion, which encompasses various features and technologies. The article highlights how increasing the number of tables and complex schedules can lead to inefficiencies in data processing. As teams expand and schedules become more frequent, the risk of running unnecessary models rises. State-aware orchestration addresses these challenges by providing a more efficient way to handle task dependencies and schedules. By introducing a clearer syntax for scheduling tasks and managing state, analysts can avoid the hassle of manually adjusting schedules and ensure resources are used more effectively. One practical implementation discussed involves storing the state of tables separately to minimize processing costs. Instead of running full scans of data every time, the orchestrator can check the stored state to determine what needs to be processed. This method not only saves time and resources but also helps mitigate issues stemming from erratic data arrivals or forgotten models. The article presents state-aware orchestration as a solution that simplifies the orchestration process, ultimately making data workflows more reliable and less prone to human error.