Each of these tools in your pipeline would need to use that tool’s associated job scheduler. In a one-off scenario, this approach will work.īut what happens when you’re not exclusively using AWS for your data pipeline? Often, you wind up needing a different job scheduler for each data tool used along your pipeline.įor example, let’s say your pipeline runs across AWS, Azure, Informatica, Snowflake, Databricks, and PowerBI. Each of the above-described methods typically requires a third-party scheduler to send the trigger.įor example, if you’re a developer who wants to trigger a DAG when a file is dropped into an AWS S3 bucket, you may opt to use AWS Lambda to schedule the trigger. Triggering a DAG based on a system event from a third-party tool remains complex. Limitations to Event-Based Automation in Airflow Trigger a DAG when a Kafka or ASW SQS event is received.Trigger a DAG when a data file is dropped into a cloud bucket.Trigger a DAG when someone fills in a website form.In the original Airflow, it was considered experimental.Ī few examples of what you might automate using sensors, deferrable operators, or Airflow’s API include: Airflow API: Used when the trigger event is truly random. In other words, it’s the most reliable and low-cost method of monitoring system events in third-party applications outside of Airflow. It’s worth noting that In Airflow 2, the API is fully supported.Deferrable operators are put in place so you don’t have to leave a long-running sensor up all day, or forever, which would increase compute costs. Deferrable Operators: An option available to use when sensors, explained above, are ideal but the time of the system event is unknown.A practical example is if you need to process data only after it arrives in an AWS bucket.
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