Question about Dataflow Gen2 runtime remapping across multiple tables

[Johanny_O]

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?

[deborshi_nag]

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