Fabric or ADF dataflows?

Question

Hi

I’m currently evaluating whether to use Fabric Data Flow or Azure Data Factory Data Flow for a specific data transformation project. In my scenario, I need to process and transform large volumes of sales data from multiple sources (including SQL databases and APIs) on a daily basis. The transformations involve complex aggregations and filtering to prepare the data for reporting.

Given the need for scalability, performance, and ease of integration with other Azure services, which option would be better suited for my requirements? Are there particular features or advantages of one over the other that I should consider? Any insights or experiences would be greatly appreciated.
Thanks

Answer 1

@Rubens Max
Thank you for the question and using Microsoft Q&A platform.

Both Fabric Data Flow and Azure Data Factory Data Flow are powerful tools for data transformation, but they have some differences that might make one more suitable for your specific needs.

Azure Data Factory:

Scalability and Performance: ADF is well-known for its scalability and performance. It can handle large volumes of data efficiently and is designed to work seamlessly with other Azure services. ADF Data Flow is built on top of Azure Databricks and can scale more efficiently to handle massive datasets, providing better performance and throughput.

Integration: ADF integrates well with a wide range of Azure services, including Azure SQL Database, Azure Synapse Analytics, and Azure Data Lake Storage. This makes it a robust choice for complex data workflows.

Complex Transformations: ADF supports complex transformations and has a rich set of built-in activities for data movement and transformation. It also supports custom code execution using Azure Functions or Databricks.

Fabric Data Flow:

Unified Data Platform: Fabric Data Flow is part of the Microsoft Fabric ecosystem, which offers a unified data platform. This can simplify the integration and management of data across different services like Lakehouse and Datawarehouse.

User-Friendly Interface: Fabric Data Flow (Dataflow Gen2) provides a more user-friendly experience with a low-code interface, making it easier to build and manage data transformations.

Ongoing Enhancements: Fabric Data Flow is continuously being enhanced with new features and capabilities, which might offer more flexibility and functionality in the future.

Given your need to process and transform large volumes of sales data from multiple sources daily, here are some points to consider:

1. Scalability and Performance: If your primary concern is handling large volumes of data with high performance, ADF might be the better choice due to its proven scalability and performance.
2. Ease of Use: If ease of use and a user-friendly interface are more important, especially for complex aggregations and filtering, Fabric Data Flow could be advantageous.
3. Integration Needs: Consider the specific Azure services you need to integrate with. ADF has a broader range of integrations, but Fabric Data Flow’s integration with the unified data platform might simplify your workflows.

You can also refer to the below links for more help:
https://learn.microsoft.com/en-us/fabric/data-factory/guide-to-dataflows-for-mapping-data-flow-users
https://learn.microsoft.com/en-us/fabric/data-factory/compare-fabric-data-factory-and-azure-data-factory
https://learn.microsoft.com/en-us/fabric/data-factory/transform-data
https://synapsefabric.com/microsoft-fabric-data-factory-vs-azure-data-factory-evolution-unveiled/
https://blog.fabric.microsoft.com/en-us/blog/data-factory-spotlight-dataflows-gen2/

Hope this helps. Do let us know if you have any further queries.

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