SharePoint Excel ingestion, Dataflow Gen2 vs shortcut + notebook CU efficiency

I wouldn't assume shortcut + notebook will automatically consume fewer CUs. It depends on the number and size of the Excel files, the transformation complexity, and how long the Spark processing runs.

For a smaller set of Excel files with standard transformations like filtering, joins, type changes and column cleanup, I would normally use Dataflow Gen2. It's simpler to build and easier to maintain.

If there are many files or the transformation logic becomes more complex, shortcut - Lakehouse - PySpark - Delta can be a better approach because it gives more control over the processing. But Spark also consumes Fabric capacity, so I wouldn't choose the notebook approach purely for CU savings.

I would also watch for SharePoint throttling if multiple jobs are accessing the folder in parallel.

For the CU comparison, the best option is to run both approaches with the same files and schedule and compare the actual consumption in the Fabric Capacity Metrics app.

For a typical Excel-folder ingestion, I would start with Dataflow Gen2 and move to the notebook approach when the file volume or transformation complexity justifies it.