Save costs with Azure Data Factory

A typical project following any industry standard process for building Azure Data Factory (ADF) pipelines will have different environments as part of development lifecycle. They might be named Development, Staging, Production, and so on. This article describes an approach to save costs from running ADF pipelines using Triggers.

Typical ADF environments will have pipelines ingesting, transforming (Data flows/Notebooks) and loading data to targets. These are scheduled using different triggers and can be Started or Stopped. Apparently, the triggers run at scheduled frequencies if in the started status and do not run if stopped.

I created a test trigger for this