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Use asset-aware scheduling when a downstream DAG should become eligible after upstream data is successfully updated. Use cron when the DAG must run at recurring wall-clock times. If it needs both behaviors, Airflow provides AssetOrTimeSchedule. Before changing a cron schedule, check the Airflow version and timetable: Airflow 2.x and Airflow 3 use different defaults for cron data intervals.
Assets or cron: what should trigger the DAG?
The choice is about what makes a DAG run eligible, not simply which schedule syntax looks simpler. Assets model data readiness between producers and consumers; cron models a recurring time cadence.
| Decision | Asset-aware schedule | Cron or time schedule |
|---|---|---|
| Run trigger | A successful update to a declared asset, subject to the DAG’s asset condition. | A cron tick or preset cadence. |
| What it models | Dependency or readiness between data-producing and data-consuming workflows. | Recurring wall-clock timing. |
| Multiple upstream inputs | A list of assets waits until each listed asset has updated at least once since the consumer DAG last ran. | Time determines the run; upstream readiness must be handled separately if needed. |
| Question to ask | Which successful data update should make this consumer eligible? | At what recurring times should this DAG run? |
Apache Airflow describes the feature this way: “In addition to scheduling Dags based on time, you can also schedule Dags to run based on when a task updates an asset.” See the Airflow asset-aware scheduling guide.
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When should you schedule a DAG on assets?
Choose assets when downstream work should follow successful upstream data production rather than an independent clock. Asset-aware scheduling was added in Airflow 2.4, according to the official guide.
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How an asset update triggers a consumer
A producer task declares an asset as an outlet. A consumer DAG lists that asset in its schedule. When the producer task completes successfully and updates the asset, the consumer can be scheduled. A failed or skipped task does not update the asset, so it does not trigger that consumer through the update.
When the consumer needs several inputs
Listing multiple assets expresses a readiness condition: all listed assets must have updated at least once since the consumer DAG’s previous run. If one asset updates repeatedly while the DAG waits for another, those repeated updates do not necessarily produce a separate consumer run each time. This pattern suits a pipeline that should wait for a group of inputs before processing them together.
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When is cron the better fit?
Choose cron when the requirement is a predictable recurring cadence—for example, run at set times whether or not a new upstream asset event has arrived. Airflow accepts cron expressions and presets such as @daily; a DAG can also use a datetime.timedelta schedule. Cron expressions are parsed with croniter, including extended syntax. See Airflow’s cron and time interval documentation.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11A cron expression specifies timing, but it does not by itself tell you what data interval a run represents. Airflow handles cron schedules through timetables, and the timetable affects interval and logical-date semantics.
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Cron trigger timetable versus cron data-interval timetable
CronTriggerTimetabletriggers at cron times and does not create a nonzero data interval: the interval’s start and end are the trigger time.CronDataIntervalTimetablecreates intervals between cron points and triggers at the end of each interval.
These choices can affect logical dates and run IDs. Airflow’s timetable documentation states that the default for [scheduler] create_cron_data_intervals is False in Airflow 3 and True in Airflow 2.x. If you are migrating or changing versions, explicitly select the intended behavior—by pinning the setting on both sides or using an explicit timetable—rather than assuming a bare cron string preserves the same semantics.
Can an Airflow DAG run on both an asset update and a schedule?
Yes. Airflow documents AssetOrTimeSchedule, which combines a time-based timetable with an asset condition and creates scheduled and asset-triggered runs independently. Use it when the workflow must react to new data and also perform periodic work, such as a scheduled check that should happen even if no asset update occurred. See the timetable guide.
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Before adopting both paths, decide whether the two kinds of runs may process overlapping data. Check that the DAG is idempotent where needed, and verify which data window each run should process; an event-triggered run and a time-triggered run should not be assumed to have identical interval semantics.
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- Identify the business trigger. If a successful upstream data update should make the consumer eligible, use assets. If the clock should trigger the run regardless of new data, use cron.
- Check input readiness. If every upstream input must be available, use the documented list-of-assets condition and account for the fact that repeated updates to one input while waiting do not necessarily create separate downstream runs.
- Confirm interval expectations. If processing depends on a defined time window, choose the timetable semantics deliberately; do not treat the cron expression alone as a description of data coverage.
- Check the Airflow major version. Bare cron strings default to different timetable behavior in Airflow 2.x and Airflow 3. Pin the desired behavior explicitly when consistency across versions matters.
- Use both only for two real requirements. If the DAG needs event responsiveness plus a periodic run, consider
AssetOrTimeScheduleand test run overlap, idempotency, and processing windows against the workload.
External events are a separate scheduling question
Asset-aware scheduling described above concerns asset updates within Airflow workflows. For external signals—such as a message from another system or a storage change—Airflow 3.0 added event-driven scheduling approaches, including push-based event delivery and pull-based asset watchers. These are relevant when an external event, rather than completion of an Airflow producer task, should initiate work. See Airflow’s event-driven scheduling documentation.
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