Azure Data Factory in Microsoft Fabric: Microsoft Fabric’s Azure Data Factory (ADF) has evolved into a robust data integration platform, offering a myriad of features that empower organizations to seamlessly manage and transform their data. In this blog post, we’ll explore the key components and capabilities of Azure Data Factory in Fabric, focusing on its unique offerings compared to traditional ADF.
Data Pipeline in Fabric: A Unified Approach
Unified Data Platform Integration
One of the standout features of Data Factory in Fabric is its seamless integration with the unified data platform, encompassing Lakehouse, Data Warehouse, and more. This integration ensures a cohesive environment for building, managing, and transforming data within a single platform.
Mapping Dataflow Gen2
Dataflow Gen2 enhances the data transformation experience within Data Factory in Fabric. With ongoing efforts to support more functions, Dataflow Gen2 provides a user-friendly interface for building complex data transformations, offering an improved and efficient experience.
Expanded Activities
Data Factory in Fabric is expanding its repertoire of activities, with a particular highlight on the newly introduced Office 365 Outlook activity. This addition enables users to configure intuitive and customizable email notifications, enhancing communication about pipeline and activity information.
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Core Components and Comparisons
Dataset and Linked Service
While traditional ADF relies on the concepts of datasets, Data Factory in Fabric takes a different approach. Instead of datasets, it uses connections for connecting to each data source and pulling data. Linked services in Fabric, similar to connections, offer a more intuitive way to create and manage data connections.
Triggers and Schedules
Data Factory in Fabric supports scheduling pipelines using the schedule trigger, automating the execution of pipelines at specified intervals. The platform is actively working on incorporating more triggers to align with ADF’s capabilities in Microsoft Fabric.
Autoresolve and Integration Runtimes
In Fabric, the concept of Integration Runtimes does not exist. Instead, Fabric focuses on providing a simplified environment without the need for runtime management.
Self-hosted Integration Runtimes
The capability for self-hosted integration runtimes, often facilitated by the On-premises Data Gateway, is still in the design phase for Fabric. This feature aims to enhance connectivity with on-premises data sources.
Azure-SSIS Integration Runtimes and Future Capabilities
The roadmap and design for Azure-SSIS Integration Runtimes and additional features such as MVNet and Private Endpoints are yet to be determined for Fabric, representing areas of ongoing development.
Operational Efficiencies and Modern Experiences
Expression Language
The expression language remains consistent across ADF and Fabric, ensuring familiarity and ease of use for users transitioning to the Fabric environment.
Authentication in Linked Service
Authentication kind in Fabric pipeline already supports popular authentication types in ADF, with plans to add more authentication kinds in the future.
CI/CD Capabilities
Fabric Data Factory is set to introduce CI/CD capabilities soon, enabling organizations to streamline their development, testing, and deployment processes.
Export and Import ARM
The Save As feature in Fabric pipeline serves a similar purpose to Export and Import ARM, providing a convenient way to duplicate pipelines for various development purposes.
Monitoring and Run History
The monitoring hub in Fabric offers a more advanced and modern experience, allowing users to gain insights across different workspaces for comprehensive monitoring and run history analysis.
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Exciting Features of Data Pipeline in Microsoft Fabric
Lakehouse/Datawarehouse Integration
Data Factory in Fabric offers native integration with Lakehouse and Data Warehouse, streamlining project development integrated with these data sources.
Office 365 Outlook Activity
The Office 365 Outlook activity provides a simple way to configure and send customized email notifications, improving communication about pipeline and activity status.
Get Data Experience
Fabric provides a modern and user-friendly Get Data experience, facilitating a quick setup for copy pipelines and connection creation.
Modern Monitoring Experience
The combination of the monitoring hub and Data Factory items allows users to gain a full view of all workloads, offering a convenient cross-workspace analysis through the monitoring hub.
Save As
The Save As feature in Fabric pipeline provides a convenient and efficient way to duplicate existing pipelines for various development purposes.
FAQs for Azure Data Factory in Microsoft Fabric
- Q: What is the key difference between Azure Data Factory (ADF) in Microsoft Fabric and traditional ADF?A: Azure Data Factory in Microsoft Fabric represents an evolution with a focus on unified data platform integration, modern monitoring experiences, and enhanced activities. Unlike traditional ADF, Fabric offers a more streamlined approach to data pipeline management.
- Q: How does Fabric handle datasets compared to traditional ADF?A: Unlike traditional ADF, Data Factory in Fabric doesn’t utilize the concept of datasets. Instead, connections are employed for linking to each data source and pulling data, providing a more intuitive way to create and manage connections.
- Q: What new activities are introduced in Azure Data Factory in Microsoft Fabric?A: Fabric introduces new activities, with a notable addition being the Office 365 Outlook activity. This allows users to configure customized email notifications about pipeline and activity information, enhancing communication.
- Q: Is there a self-hosted integration runtime capability in Azure Data Factory in Microsoft Fabric?A: The capability for self-hosted integration runtimes, facilitated by the On-premises Data Gateway, is still in the design phase for Fabric. This feature aims to enhance connectivity with on-premises data sources.
- Q: How does Fabric handle triggers and scheduling compared to traditional ADF?A: Fabric supports scheduling pipelines using the schedule trigger, allowing automatic execution at specified intervals. The platform is actively working on incorporating more triggers to align with ADF’s capabilities in Microsoft Fabric.
- Q: Are there any differences in the expression language used in Azure Data Factory in Microsoft Fabric?A: The expression language remains consistent across ADF and Fabric, ensuring familiarity and ease of use for users transitioning to the Fabric environment.
- Q: What advantages does Fabric offer in terms of monitoring and run history compared to traditional ADF?A: Fabric provides a more advanced and modern monitoring hub, offering a full view of all workloads and enabling cross-workspace analysis for enhanced insights and analytics.
- Q: How does Fabric handle authentication in linked services compared to traditional ADF?A: Authentication kind in Fabric pipeline already supports popular authentication types in ADF, with plans to add more authentication kinds in the future.
- Q: Is there a CI/CD capability in Azure Data Factory in Microsoft Fabric?A: CI/CD capability in Fabric Data Factory is in development and expected to be available soon, allowing organizations to streamline their development, testing, and deployment processes.
- Q: Can existing pipelines be duplicated in Azure Data Factory in Microsoft Fabric?A: Yes, the Save As feature in Fabric pipeline provides a convenient way to duplicate existing pipelines for various development purposes.
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Conclusion
In conclusion, the data pipeline in Microsoft Fabric’s Azure Data Factory represents a significant leap forward in terms of unified data platform integration, enhanced activities, and modern operational experiences. As organizations leverage the power of Fabric for their data integration needs, the platform continues to evolve, introducing new features and capabilities to stay at the forefront of the data management landscape. Explore the exciting features of Data Factory in Fabric and unlock the potential for seamless and efficient data workflows in your organization. Happy data integrating!