Fabric changes
Microsoft's edit ·
Microsoft's commit message in MicrosoftDocs/fabric-docs · commit 0ac0f42 · +1 −1 lines · also on GitHub
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|**JobInsight Diagnostics Library (Preview)**|[JobInsight](https://blog.fabric.microsoft.com/blog/gain-deeper-insights-into-spark-jobs-with-jobinsight-in-microsoft-fabric?ft=All) is a diagnostics library to analyze completed Spark applications via APIs for queries, jobs, stages, tasks, executors, and event logs. For more information, see [Job Insight Library (Preview)](../data-engineering/job-insight-library.md).|
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|**Lakehouse auto-binding in Git (Preview)**|[Lakehouse auto-binding in Git (Preview)](https://blog.fabric.microsoft.com/blog/fabric-notebooks-support-lakehouse-auto-binding-in-git-preview?ft=All) resolves lakehouse references automatically as notebooks move across Git-connected workspaces, making notebooks portable across development, test, and production environments. For more information, see [Notebook source control and deployment](../data-engineering/notebook-source-control-deployment.md).|
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|**Lakehouse support for git integration and deployment pipelines (Preview)**|The [Lakehouse now integrates with the lifecycle management capabilities in Microsoft Fabric](https://blog.fabric.microsoft.com/blog/microsoft-fabric-november-2023-update/#post-5122-_Toc1381410242), providing a standardized collaboration between all development team members throughout the product's life. [Lakehouse git integration and deployment pipelines](../data-engineering/lakehouse-git-deployment-pipelines.md) facilitates an effective product versioning and release process by continuously delivering features and bug fixes into multiple environments.|
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−|**Lakehouse utility suite (Preview)**|The [Lakehouse Utility Suite](https://blog.fabric.microsoft.com/blog/modernizing-pipelines-new-activities-and-innovations-in-fabric-data-factory-pipelines?ft=All) now includes the [Lakehouse maintenance activity (Preview)](https://aka.ms/LakehouseMaintenanceDocs), to automate routine Lakehouse upkeep tasks, and the [Refresh SQL endpoint activity (Preview)](https://aka.ms/RSQLDocs), to refresh the Lakehouse SQL analytics endpoint on demand or on a schedule within Fabric pipelines. |
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+|**Lakehouse utility suite (Preview)**|The [Lakehouse Utility Suite](https://blog.fabric.microsoft.com/blog/modernizing-pipelines-new-activities-and-innovations-in-fabric-data-factory-pipelines?ft=All) now includes the [Lakehouse maintenance activity (Preview)](https://aka.ms/LakehouseMaintenanceDocs), to automate routine Lakehouse upkeep tasks, and the [Refresh SQL analytics endpoint activity](https://aka.ms/RSQLDocs), to refresh the Lakehouse SQL analytics endpoint on demand or on a schedule within Fabric pipelines. |
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|**Load Fabric OneLake Data in Excel**|Easily load Fabric OneLake data into Excel with integrated OneLake catalog and modern Get Data experience (preview). For more information and steps to get started, see [OneLake catalog and Get Data are integrated into Excel for Windows](https://blog.fabric.microsoft.com/blog/easily-load-fabric-onelake-data-into-excel-onelake-catalog-and-modern-get-data-are-integrated-into-excel-for-windows-preview?ft=All).|
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|**Manage failure notifications from the monitoring hub in Fabric (Preview)**|The [Schedule failures page in the Fabric monitoring hub (Preview)](https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Manage-failure-notifications-from-the-monitoring-hub-in-Fabric/ba-p/5160381) lists every scheduled item with failure notifications configured and lets you add, edit, or remove recipients for any item from one place, without opening each item individually. For more information, see [Schedule failures (preview)](../admin/monitoring-hub.md#schedule-failures-preview) and [Receive notifications for failed scheduled jobs](job-scheduler.md#receive-notifications-for-failed-scheduled-jobs).|
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|**Mapping Data Flow Transforms in Dataflow Gen2 (Preview)**|[Mapping data flow transforms in Dataflow Gen2 (Preview)](../data-factory/dataflow-gen2-mapping-data-flows-transforms.md) introduces Spark-based transformation capabilities directly in the Dataflow Gen2 creation experience, enabling large-scale data transformations with Spark compute and migration of mapping data flows to Fabric. For pricing details, see [Dataflow Gen2 pricing](../data-factory/pricing-dataflows-gen2.md).|
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Microsoft's Markdown source from MicrosoftDocs/fabric-docs, © Microsoft Corporation, under CC BY 4.0. Changed lines with up to 3 unchanged lines around each; ChangeIntel kept this copy 9 Oct 22:05 UTC. The commit date is when the source changed, which can be hours or days before Learn published it.