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Upgrade to PremiumQ1. What makes a Dataset "update" from Airflow's perspective?
A task instance that lists the Dataset in its outlets=[] completing successfully. Airflow doesn't inspect the actual data - the update event is purely "this task, which claimed to write this Dataset, succeeded."
Q2. A DAG is scheduled with schedule=[dataset_a, dataset_b]. Dataset A updates three times in a row with no update to Dataset B in between. How many times does the DAG run?
Zero times, so far. A DAG scheduled on multiple Datasets requires all of them to have updated at least once since its last run - repeated updates to only one Dataset don't satisfy that until Dataset B also updates.
Q3. What changed between Airflow 2.x Datasets and Airflow 3.0 Assets beyond the name?
AssetWatchers - the ability for an Asset to react to changes happening outside Airflow entirely (a queue message, a webhook), not only to an Airflow task explicitly declaring an update via outlets=.
Q4. Between TriggerDagRunOperator and ExternalTaskSensor, which one requires the upstream DAG to know the downstream DAG's ID, and which requires the downstream DAG to know the upstream one's?
TriggerDagRunOperator lives in the upstream DAG and needs trigger_dag_id - the upstream DAG knows about downstream. ExternalTaskSensor lives in the downstream DAG and needs external_dag_id - the downstream DAG knows about upstream. Datasets need neither DAG to know the other's ID at all.
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