I’d start by testing informatica-sparker, a Python tool that aims to convert PowerCenter XML exports into baseline PySpark scripts. It could save time extracting mappings and translating common transformations, but I’d test it on a few real workflows...
I’d check the job configuration and execution context first. A notebook run and an API-triggered run might use different parameters, permissions, environment variables, or even a different cluster state. Cold starts can also affect timing, especially...
Yes, DAB is a good approach if the new workspace is already built and you mainly need to migrate notebooks, jobs, pipelines, and other workspace resources. You can use databricks bundle generate to create configuration for existing resources, put it ...
