Inside Databricks, the execution of a selected unit of labor, initiated routinely following the profitable completion of a separate and distinct workflow, permits for orchestrated information processing pipelines. This performance permits the development of advanced, multi-stage information engineering processes the place every step depends on the end result of the previous step. For instance, a knowledge ingestion job might routinely set off a knowledge transformation job, making certain information is cleaned and ready instantly after arrival.
The significance of this function lies in its skill to automate end-to-end workflows, decreasing handbook intervention and potential errors. By establishing dependencies between duties, organizations can guarantee information consistency and enhance general information high quality. Traditionally, such dependencies have been typically managed via exterior schedulers or customized scripting, including complexity and overhead. The built-in functionality inside Databricks simplifies pipeline administration and enhances operational effectivity.
