Trying to represent and schedule pipelines of data assets as workflows is painful, because workflows are an awkward metaphor for data pipelines. A single data s

Trying to represent and schedule pipelines of data assets as workflows is painful, because workflows are an awkward metaphor for data pipelines. A single data source can power dozens of data products, each with different data freshness requirements. A single data product can consume data from dozens of different sources, each of which is updated at a different cadence. Trying to chop up this graph of data assets into discrete workflows that each get executed synchronously often feels like forcing a square peg into a round hole. Dagster 1.1 introduces a system for scheduling data pipelines that lets you escape writing workflows entirely. Instead, you directly specify how up-to-date you expect each asset to be, as well as how to determine whether source data has changed. Dagster then automatically schedules asset materializations to ensure that data arrives on time, while avoiding unnecessary computation.