Azure Data Factory vs. Data Factory in Microsoft Fabric: Microsoft Fabric has introduced a new era in data integration with its next-generation Azure Data Factory, known as Data Factory in Microsoft Fabric. This powerful cloud-based platform is designed to handle data movement and transformation services at an enterprise scale, simplifying the user experience and providing robust capabilities. In this comprehensive article, we will delve deep into the differences between Azure Data Factory and Data Factory in Microsoft Fabric, and provide a detailed comparison table to help you make an informed choice. Additionally, we will explore external links and FAQs related to these services, offering a complete guide for data enthusiasts.
Understanding the Transition
Microsoft Fabric has built Data Factory in Microsoft Fabric on the foundation of Azure Data Factory, with the primary goal of simplifying the user experience, empowering users with robust capabilities, and offering true enterprise-grade solutions. Let’s dive into the key differences between these two data integration services.
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Key Differences
| Feature | Azure Data Factory | Data Factory in Microsoft Fabric |
|---|---|---|
| Pipeline Integration | Data pipelines are a core concept. | Data pipelines are better integrated with the unified data platform, including Lakehouse, Datawarehouse, and more. |
| Mapping Dataflow | Dataflow Gen2 is used for building data transformation processes. | Dataflow Gen2 provides a more user-friendly experience for data transformation. |
| Activities | Azure Data Factory is actively working on expanding the range of activities available. | Data Factory in Microsoft Fabric is also working on supporting more Azure Data Factory activities. Additionally, it introduces new activities like the Office 365 Outlook activity for enhanced versatility. |
| Dataset Concept | Azure Data Factory employs the concept of datasets for organizing and managing data. | In Fabric, the concept of datasets is no longer in use. Instead, connections serve as a way to link to various data sources and extract the required data, streamlining the process. |
| Linked Service Connections | Linked service connections provide functionality for connecting to data sources. | Connections have similar functionality as linked service, but connections in Fabric have a more intuitive way to create. |
| Triggers | Triggers in Azure Data Factory include schedules, with other triggers still under development. | Fabric utilizes schedules for triggering pipeline execution automatically. Just like in Azure Data Factory, Fabric is actively working on introducing additional triggers for enhanced functionality. |
| Publishing Process | In Azure Data Factory, publishing is required to save changes to your pipeline. | In Fabric, you can save your pipeline content without the need for publishing. Simply use the “Save” button to retain your changes, and when you run the pipeline, it will automatically save the content before execution. |
| Autoresolve and Azure Integration Runtime | Azure Data Factory includes the concept of Integration runtime for data processing. | In Fabric, the concept of Integration runtime is not present, indicating a shift in data processing methodology. |
| Self-hosted Integration Runtimes | Azure Data Factory utilizes the On-premises Data Gateway for self-hosted integration runtimes. | Fabric is still in the process of designing its capabilities for self-hosted integration runtimes, indicating ongoing development in this area. |
| Azure-SSIS Integration Runtimes and MVNet/Private Endpoint | The roadmap and design for Azure-SSIS integration runtimes and MVNet/Private Endpoint support are yet to be confirmed. | Similarly, Fabric has not finalized the roadmap and design for these features, signaling upcoming developments. |
| Expression Language | The expression language used in Azure Data Factory is also maintained in Fabric, offering consistency and familiarity for users. | The expression language is similar in both Azure Data Factory and Data Factory in Microsoft Fabric, ensuring a smooth transition for users. |
Benefits of Data Factory in Microsoft Fabric
The transition from Azure Data Factory to Data Factory in Microsoft Fabric comes with several benefits:
- Unified Data Platform Integration: Data pipelines in Fabric are better integrated with a unified data platform, which includes Lakehouse and Datawarehouse. This integration allows for a more comprehensive approach to data processing.
- Enhanced Data Transformation: Dataflow Gen2 in Fabric offers an improved user experience for building data transformation processes, making it easier for users to create and manage transformations.
- Additional Activities: While Azure Data Factory is expanding its range of activities, Fabric introduces new activities like the Office 365 Outlook activity, enhancing the versatility of data workflows.
- Streamlined Data Access: The removal of dataset concepts in Fabric simplifies data access by using connections to link to various data sources. This provides a more intuitive way to manage data sources and access required data.
- Intuitive Connection Creation: Linked service connections in Fabric offer a more intuitive way to create and manage data connections, enhancing the user experience.
- Expanded Trigger Support: Data Factory in Microsoft Fabric utilizes schedules for triggering pipeline execution, with ongoing development to introduce additional triggers. This ensures that your data workflows can be automatically initiated based on various conditions.
- Efficient Content Management: Fabric eliminates the need for publishing pipelines to save changes. Users can directly save content using the “Save” button, providing a more streamlined content management process.
- Simplified Data Processing: The absence of Integration runtime in Fabric simplifies data processing, making it more accessible for users.
- Future-Ready Capabilities: Although features like self-hosted integration runtimes, Azure-SSIS integration runtimes, MVNet/Private Endpoint support are still in development, the transition to Fabric ensures that users will have access to these features in the future.
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External Links and FAQs
Explore the world of Azure Data Factory and Data Factory in Microsoft Fabric with these external resources and frequently asked questions:
External Links:
Conclusion
The evolution from Azure Data Factory to Data Factory in Microsoft Fabric marks a significant leap in the world of data integration and transformation. Microsoft Fabric’s next-generation Data Factory offers a host of benefits and enhancements that cater to the growing demands of data-driven businesses.
The key differences between these two data integration services demonstrate the commitment to making data operations more user-friendly, versatile, and enterprise-grade. The better integration with unified data platforms, streamlined data transformation processes, and an expanded range of activities in Data Factory in Microsoft Fabric all contribute to a more robust data solution.