When Airflow 3.0 introduced asset watchers, event-driven scheduling became a first-class feature: a Dag could run the moment a message landed in a queue, with no sensors and no external glue code.
Since then, Airflow 3 has turned that foundation into something you can run at production scale. A single, unified trigger now covers Kafka, Amazon SQS, etc, and new event triggers extend the same pattern to HTTP APIs and Apache Iceberg tables.
In this session, you'll learn how to build and operate event-driven pipelines on the latest Airflow 3 releases, including:
- Triggering Dags from Kafka with the unified MessageQueueTrigger and using the message payload in your tasks
- Scaling to many watchers on one source with shared streams, without dropping events
- Building stateful watchers that track what they have already processed with the asset state store
- Choosing between watchers and API-triggered runs, and where Sub-Second Pipelines on Astro fit
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