Microsoft Fabric Data Factory vs. Azure Data Factory: Evolution Unveiled

Microsoft Fabric Data Factory vs. Azure Data Factory: In the dynamic landscape of data integration, Microsoft is reshaping the narrative with the evolution of Azure Data Factory (ADF) into Data Factory in Fabric. This comprehensive exploration aims to dissect the unique features, capabilities, and ongoing enhancements in both Microsoft Fabric Data Factory and the traditional Azure Data Factory, providing a roadmap for organizations seeking the optimal solution for their data processing needs.

Unveiling the Evolution:

1. Pipeline Dynamics:

  • Azure Data Factory Pipeline: The traditional ADF pipeline serves as a structured workflow for orchestrating data movement and transformations.
  • Data Factory in Fabric Pipeline: Evolving beyond, Fabric pipelines are seamlessly integrated with the unified data platform, offering a unified experience across Lakehouse, Data Warehouse, and more.

2. Transformation with Dataflow:

  • Mapping Dataflow in ADF: The foundational mapping dataflow is the backbone for transformations within the data pipeline.
  • Dataflow Gen2 in Fabric: The introduction of Dataflow Gen2 in Fabric promises a more user-friendly experience for building transformations, with ongoing efforts to support additional functions.

3. Reimagined Activities:

  • Activities in ADF: Traditional ADF activities are integral components, facilitating diverse data processing tasks.
  • Activities in Fabric: Ongoing progress in Data Factory in Fabric aims to broaden activity support, introducing new additions such as the Office 365 Outlook activity for enhanced data processing capabilities.


4. Dataset Departure:

  • Datasets in ADF: ADF relies on the concept of datasets to represent structured sets of data within the pipeline.
  • No Datasets in Fabric: Surprisingly, Data Factory in Fabric deviates from the traditional dataset concept, opting for a more direct connection-based approach for data retrieval.

5. Connections in Focus:

  • Linked Service in ADF: ADF uses linked services to connect to external data sources and destinations.
  • Connections in Fabric: Fabric introduces connections as a more intuitive way to create and manage links to data sources, simplifying the connection process.

Triggers, Publishing, and Runtimes:

1. Triggers and Schedules:

  • Triggers in ADF: Traditional ADF triggers include schedules, with ongoing work to introduce additional triggers.
  • Schedules in Fabric: Fabric leverages schedules to automatically run pipelines, with a commitment to support more triggers from ADF in Microsoft Fabric.

2. Publishing Paradigm:

  • Publish in ADF: In ADF, publishing is essential for saving changes and running the pipeline.
  • Save and Run in Fabric: Data Factory in Fabric introduces a more efficient paradigm where saving the content is separated from running the pipeline, providing a streamlined workflow.

3. Integration Runtimes and Hosting:

  • Integration Runtimes in ADF: ADF introduces concepts like autoresolve and Azure Integration runtime for orchestrating data integration.
  • No Integration Runtimes in Fabric: Interestingly, Data Factory in Fabric discards the concept of integration runtimes, simplifying the overall architecture.

4. Self-hosted Runtimes and Future Capabilities:

  • Self-hosted Runtimes in ADF: ADF facilitates self-hosted integration runtimes, with ongoing efforts for on-premises data gateway integration.
  • In Design in Fabric: The capability of self-hosted runtimes in Fabric is still in the design phase, indicating potential enhancements in future releases.

5. Azure-SSIS Integration Runtimes and Future Features:

  • To Be Determined in ADF: The roadmap and design for Azure-SSIS integration runtimes remain undetermined in traditional ADF.
  • Undecided in Fabric: Similarly, the capabilities for Azure-SSIS integration runtimes, as well as future features like MVNet and Private Endpoints, are yet to be determined in Data Factory in Fabric.

Expression Language, Authentication, and CI/CD:

1. Expression Language Consistency:

  • Expression Language in ADF: A powerful tool for dynamically configuring pipeline components, ADF maintains a consistent expression language.
  • Expression Language in Fabric: Data Factory in Fabric maintains this consistency, ensuring a seamless transition for users familiar with ADF.

2. Authentication in Linked Services and Connections:

  • Authentication in ADF: ADF employs authentication types within linked services to secure connections.
  • Authentication in Fabric: Data Factory in Fabric adopts a similar approach, supporting popular authentication types in ADF pipelines with a commitment to expanding the variety.

3. CI/CD Capabilities:

  • CI/CD in ADF: Continuous Integration and Continuous Deployment (CI/CD) capabilities are intrinsic to ADF, ensuring streamlined development workflows.
  • Upcoming in Fabric: The CI/CD capability in Data Factory in Fabric is anticipated soon, promising enhanced deployment and management functionalities.

Export and Import, Monitoring, and Advanced Features:

1. Export and Import Methods:

  • Export and Import in ADF: Traditional ADF relies on the ARM template for export and import.
  • Save As in Fabric: Data Factory in Fabric introduces the “Save As” feature in the pipeline, allowing duplication without the need for explicit export and import.

2. Monitoring Hub and Advanced Features:

  • Monitoring in ADF: ADF provides monitoring and run history features for tracking pipeline executions.
  • Advanced Monitoring in Fabric: The monitoring hub in Data Factory in Fabric boasts more advanced functions and a modernized experience. It offers insights across different workspaces for a holistic view of data workflows.


External Links:

  1. Azure Data Factory Documentation
  2. Azure Data Factory in Fabric Documentation

Frequently Asked Questions (FAQs):

Q1: Can I migrate my existing ADF pipelines to Fabric seamlessly?

  • While some concepts may differ, Microsoft provides tools and documentation to facilitate a smooth migration process from ADF to Data Factory in Fabric.

Q2: How does the absence of datasets impact data retrieval in Fabric?

  • In Fabric, the absence of datasets simplifies the data retrieval process. Connections are used directly to link to each data source and pull data as needed.

Q3: Will Fabric eventually replace traditional ADF?

  • The evolution of Fabric is aimed at providing a more integrated and unified experience. However, the adoption of Fabric depends on specific use cases and organizational requirements.

Q4: How does Fabric impact existing ADF users?

  • Users familiar with ADF will find a certain level of consistency in Fabric. The changes introduced are aimed at improving efficiency and integration with the unified data platform.

Q5: When can we expect the CI/CD capabilities in Fabric?

  • The CI/CD capabilities in Data Factory in Fabric are slated to be introduced soon, providing users with enhanced deployment and management functionalities.


The shift from Azure Data Factory to Data Factory in Fabric represents a strategic evolution in the realm of data integration. This comparison has unveiled the unique features and ongoing enhancements in both environments, offering organizations a comprehensive roadmap to navigate the changing landscape. As Microsoft continues to refine and expand both ADF and Fabric, users can anticipate a richer, more unified experience in managing and processing their data workflows. Whether it’s leveraging the traditional robustness of ADF or embracing the innovative capabilities of Fabric, organizations have the flexibility to choose the solution that best aligns with their evolving data integration needs.

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