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The Lakehouse integrates with the lifecycle management capabilities in Microsoft Fabric, providing a standardized collaboration between all development team members throughout the product's life. Lifecycle management facilitates an effective product versioning and release process by continuously delivering features and bug fixes into multiple environments. To learn more, see What is lifecycle management in Microsoft Fabric?.
Important
This feature is in preview.
The Lakehouse is an item that contains both metadata and data that is referenced in multiple objects in the workspace. Lakehouse contains tables, folders, and shortcuts as primary manageable data container items. From a development workflow perspective, the following dependent objects might reference a Lakehouse:
The default semantic model and SQL analytics endpoint metadata are related to a Lakehouse and managed by the git update process by default. As a principle data is not tracked in git, only metadata is tracked.
The following lakehouse information is serialized and tracked in a git connected workspace:
Note
The tracked logical guid is an automatically generated cross-workspace identifier representing an item and its source control representation.
Important
Only the Lakehouse container artifact is tracked in git in the current experience. Tables (Delta and non-Delta) and Folders in the Files section aren't tracked and versioned in git.
The following capabilities are available:
shortcuts.metadata.json
under the lakehouse folder in git.shortcuts.metadata.json
file.Unidentified
section until references are resolved.Important
Use caution when changing OneLake Shortcut properties directly in the shortcuts.metadata.json
file. Incorrect changes to the properties, specially GUIDs, can render the OneLake Shortcut invalid when updates are applied back to the workspace.
Important
An update from git will override the state of shortcuts in the workspace. All the Shortcuts in the workspace are created, updated or deleted based on the incoming state from git.
The Lakehouse is supported in Microsoft Fabric lifecycle management deployment pipelines. It enables environment segmentation best-practices.
Lakehouse deployment pipelines integration capabilities:
Deployment across dev, test, and production workspaces.
Lakehouse can be removed as a dependent object upon deployment. Mapping different Lakehouses within the deployment pipeline context is also supported.
If nothing is specified during deployment pipeline configuration, a new empty Lakehouse object with same name is created in the target workspace. Notebook and Spark Job Definitions are remapped to reference the new Lakehouse object in the new workspace.
If the Lakehouse dependency is configured to reference a different Lakehouse during deployment pipeline configuration time, such as the upstream Lakehouse, a new empty Lakehouse object with same name still is created in the target workspace, but Notebooks and Spark Job Definitions references are preserved to a different Lakehouse as requested.
SQL Analytics endpoints and semantic models are provisioned as part of the Lakehouse deployment.
No object inside the Lakehouse is overwritten.
Updates to Lakehouse name can be synchronized across workspaces in a deployment pipeline context.
Important
A deployment will override the state of shortcuts in the target workspace. All the Shortcuts in the target lakehouse are updated or deleted based on the state in the source lakehouse. New shortcuts are created in the target lakehouse. Always click on "review changes" to understand the changes that will be deployed between source and target workspaces.
Events
Mar 31, 11 PM - Apr 2, 11 PM
The biggest Fabric, Power BI, and SQL learning event. March 31 – April 2. Use code FABINSIDER to save $400.
Register todayTraining
Learning path
Implement a Lakehouse with Microsoft Fabric DP-601T00 - Training
This learning path introduces the foundational components of implementing a data lakehouse with Microsoft Fabric. (DP-601T00)
Certification
Microsoft Certified: Fabric Data Engineer Associate - Certifications
As a Fabric Data Engineer, you should have subject matter expertise with data loading patterns, data architectures, and orchestration processes.