Question about Dataflow Gen2 runtime remapping across multiple tables

Johanny_O — Fri, 22 May 2026 09:41:19 GMT

In Microsoft Fabric Dataflow Gen2, is it supported to use a single parameterized Dataflow for fully metadata-driven Bronze ingestion across multiple tables with different schemas?

Scenario:

Pipeline passes runtime parameters:
- p_source_schema
- p_source_table
- p_dest_schema
- p_dest_table
Dataflow source navigation is dynamic using parameters
Destination uses dynamic schema and automatic settings
Goal is:
- same DF2 reused for customers/orders/products/etc.
- automatic runtime column mapping
- all columns converted dynamically to string/text

Problem:

The Dataflow still appears to retain metadata/mapping from the first previewed table (customers)
Runtime refresh either:
- creates the wrong destination table
- or fails with generic EntityUserFailure / Mashup errors

Question:
Does DF2 truly support runtime automatic remapping for different table schemas inside a single reusable Dataflow, or is the recommended architecture:

one DF2 per table
or Notebook/Spark for metadata-driven Bronze ingestion?

Re: Question about Dataflow Gen2 runtime remapping across multiple tables

deborshi_nag — Fri, 22 May 2026 10:50:04 GMT

Hello @Johanny_O Dataflow Gen2 allows parameterisation, enabling you to execute the same process with different values each time within a pipeline. Please refer to the Microsoft documentation for details on how this works.

Parameterized Dataflow Gen2 - Microsoft Fabric | Microsoft Learn

topic Question about Dataflow Gen2 runtime remapping across multiple tables in Data Engineering

Question about Dataflow Gen2 runtime remapping across multiple tables

Re: Question about Dataflow Gen2 runtime remapping across multiple tables