Data volume Limitation With Dataflow Gen2

Hi Koritala , I am not entirely certain, but based on similar scenarios I have seen, Dataflow Gen2 is capable of handling extremely large datasets, including tables with billions of rows, provided that the overall architecture is designed appropriately.

In summary, there is no fixed limit preventing Dataflow Gen2 from ingesting more than 2 billion records.
It can load the full dataset if transformations preserve query folding and the workload is efficiently designed.
All data is stored as Delta Lake files within OneLake.

Hi Koritala ,

For tables of this size (2+ billion rows), Dataflow Gen2 should be used primarily for raw or lightly transformed data, not heavy transformations.

Recommended pattern:

Use Dataflow Gen2 to ingest raw data (or apply only minimal, schema-level transformations such as column selection, renaming, basic type casting).

Store the data in OneLake (Lakehouse/Warehouse) as Delta tables.

Perform heavy transformations, joins, aggregations, and business logic downstream using Spark, Warehouse SQL, or semantic models.

Applying complex transformations during ingestion significantly increases refresh time, compute usage, and failure risk, especially with very large datasets.

In short:

Raw / lightly transformed data → Dataflow Gen2

Complex transformations → Lakehouse / Warehouse / Spark

This aligns with Microsoft Fabric best practices for large-scale data.

Thanks,

Sai Teja 

Hi Koritala, 

While it may work with dataflows gen 2, I would highly recommend a more scaleable approach. 

I would drop the dataflows entirely if possible. Use a pipeline to ingest the data into a lakehouse, and then use spark (either pyspark or sparksql) to do your transformations. That will be the most efficient.