Business Intelligence

Comparing PowerBI DataFlows vs Dataflows Gen2 on Microsoft Fabric

Comparing PowerBI DataFlows vs Dataflows Gen2 on Fabric

The Microsoft product group recently did an AMA on Reddit on their newest and greatest Analytical Product “Microsoft Fabric” announced in the build conference.

Dataflows Gen2 brings forth a complete ETL/ELT data integration experience, revolutionizing the way businesses extract, transform, and load data. In this blog post, we delve into the exciting features and advancements that make Dataflows Gen2 a game-changer in the realm of data integration.

Tabular comparison between Dataflows vs Dataflows Gen2:

Feature Dataflows Dataflows Gen2
Integration Part of Power BI / Power Platform General-purpose Data Integration capability (beyond Power BI)
Output Destinations Limited destinations (Power BI) Multiple destinations (Fabric/Synapse Lakehouse, Warehouse, Real-Time Analytics, SQL, and more)
Performance and Scale Limited performance and scale Built on top of Fabric compute engines for improved performance and scale
Staging Uses default staging mechanism Uses Fabric Lakehouse for staging, resulting in better performance
Copy Functionality Does not support petabyte scale copy Integrates with petabyte scale copy for faster data import/copy
Monitoring Integration Not specified Fully integrates with Fabric Monitoring hub
Authoring/Save Model Overall improvements Improved authoring and save model experience
Licensing Power BI Premium Capacities Works with Fabric Capacities and Power BI Premium Capacities

Dataflows Gen2 is presented as an evolution of Dataflows with several enhancements and capabilities. It introduces output destinations, allowing the transformation results to be written to various targets.

It is built on Fabric compute engines, addressing performance and scale issues. It leverages Fabric Lakehouse for staging and integrates with monitoring. Data import/copy is made faster with the integration of petabyte scale copy.

Overall, Dataflows Gen2 aims to provide a more versatile and flexible data transformation experience with better performance and scale compared to its predecessor, Dataflows.

In Microsoft Fabric, a lakehouse is implemented using Azure Data Lake Storage, and a data model/dataset is implemented using Azure SQL Database.

Here is a table that summarizes the differences between a lakehouse and a data model/dataset:

Feature Lakehouse Data Model/Dataset
Storage architecture Single repository Separate repositories
Flexibility Very flexible Less flexible
Manageability Can be difficult to manage Easier to manage
Consistency Can be difficult to ensure consistency Consistency is easier to ensure
Accuracy Can be difficult to ensure accuracy Accuracy is easier to ensure
Suitability for different purposes Suitable for a variety of purposes Not suitable for all purposes


With the introduction of Dataflows Gen2, organizations now have access to a powerful and versatile data integration tool that goes beyond the confines of Power BI.

The ability to leverage multiple output destinations, improved performance and scale, seamless monitoring integration, and enhanced authoring capabilities make Dataflows Gen2 a vital component in the data management toolkit.

As Microsoft continues to refine and expand upon this technology, the possibilities for transforming and extracting insights from data become even more compelling. Embrace the power of Dataflows Gen2 and unlock a world of potential for your data integration needs.

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