Dagster's Declarative Automation lets you define what it means for a data asset to be 'up to date' rather than manually wiring together schedules, sensors, and triggers. The automation daemon continuously evaluates conditions against the live asset graph — including dependency state, partitions, and data versions — and determines exactly what needs to run. Built-in conditions like eager(), on_cron(), and on_missing() cover common patterns, and they can be composed with logical operators for complex policies. Partitioned assets benefit especially, as Dagster automatically resolves partition mappings (e.g., hourly-to-daily) without custom coordination code. Automation conditions can also be unit-tested with evaluate_automation_conditions() against an ephemeral instance before deployment.
