Dataflow triggers are instructions for the event framework to kick off tasks in response to events that occur in the pipeline.

For example, you can use dataflow triggers to change file permissions after a destination closes a file. Or you might use a dataflow trigger to stop a pipeline after the MySQL Query Consumer origin processes all available data.

The event framework consist of the following components:
event generation
The event framework generates pipeline-related events and stage-related events. The framework generates pipeline events only when the pipeline starts and stops. The framework generates stage events when specific stage-related actions take place. The action that generates an event differs from stage to stage and is related to how the stage processes data.
Events produce event records. Pipeline-related event records are passed immediately to the specified event consumer. Stage-related event records are passed through the pipeline in an event stream.
task execution
To trigger a task, you need an executor. Executor stages perform tasks in external systems or in StreamSets Cloud. Each time an executor receives an event, it performs the specified task.
For example, the Amazon S3 executor performs tasks in Amazon S3 each time it receives an event, and the Azure Data Lake Storage Gen2 executor performs tasks in Azure Data Lake Storage Gen2 each time it receives an event. Within StreamSets Cloud, the Pipeline Finisher executor stops a pipeline upon receiving an event, transitioning the pipeline to a Finished state.
event storage
To store event information, pass the event to a destination. The destination writes the event records to the destination system, just like any other data.
For example, you might store event records to keep an audit trail of the files that the pipeline origin reads.