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

Conclusion:

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.

Disclaimer: The Questions and Answers provided on https://www.gigxp.com are for general information purposes only. We make no representations or warranties of any kind, express or implied, about the completeness, accuracy, reliability, suitability or availability with respect to the website or the information, products, services, or related graphics contained on the website for any purpose.