Dataflows Gen2 vs Notebooks

Sanyukti_Jain — Mon, 15 Jun 2026 05:42:51 GMT

When prepping data in Fabric, how do you decide whether to use Dataflows Gen2 or a notebook (PySpark/SQL)? Is it mainly about data volume, or are there other factors?

Re: Dataflows Gen2 vs Notebooks

Parchitect — Mon, 15 Jun 2026 07:09:55 GMT

Hello!

I wouldn’t decide only by data volume.

For me it’s more about who will own it and how complex the logic is.

If the people maintaining it are analysts or Power BI developers, and the transformations are standard Power Query-style steps, I’d usually go with Dataflows Gen2. It’s easier to understand, easier to hand over, and better for low-code data prep.

If the logic needs a data engineer — PySpark/SQL, custom functions, complex joins, SCD logic, data quality checks, Delta maintenance, or reusable code — then I’d use a notebook.

So my simple rule is:

Dataflows Gen2 = analyst-friendly, visual, low-code, easier handover.
Notebooks = engineer-friendly, code-first, more control, better for complex logic.

Volume matters, but skillset, maintainability, and ownership matter just as much.

In real projects, I often use both and let a pipeline orchestrate them.

Best regards,

Parchitect - Solutions Architect

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Re: Dataflows Gen2 vs Notebooks

Sanyukti_Jain — Wed, 17 Jun 2026 15:21:34 GMT

very helpful.👏

topic Re: Dataflows Gen2 vs Notebooks in Data Engineering

Dataflows Gen2 vs Notebooks

Re: Dataflows Gen2 vs Notebooks

Re: Dataflows Gen2 vs Notebooks