🏭🗒️Microsoft Fabric: Deployment Pipelines [Notes]

Disclaimer: This is work in progress intended to consolidate information from various sources for learning purposes. For the latest information please consult the documentation (see the links below)! 

Last updated: 26-Apr-2025

[Microsoft Fabric] Deployment Pipelines

• {def} a structured process that enables content creators to manage the lifecycle of their organizational assets [5]
• enable creators to develop and test content in the service before it reaches the users [5]
• can simplify the deployment process to development, test, and production workspaces [5]
• one Premium workspace is assigned to each stage [5]
• each stage can have 
• different configurations [5]
• different databases or different query parameters [5]
• {action} create pipeline
• from the deployment pipelines entry point in Fabric [5]
• creating a pipeline from a workspace automatically assigns it to the pipeline [5]
• {action} define how many stages it should have and what they should be called [5]
• {default} has three stages
• e.g. Development, Test, and Production
• the number of stages can be changed anywhere between 2-10 
• {action} add another stage,
• {action} delete stage
• {action} rename stage 
• by typing a new name in the box
• {action} share a pipeline with others
• users receive access to the pipeline and become pipeline admins [5]
• ⇐ the number of stages are permanent [5]
• can't be changed after the pipeline is created [5]
• {action} add content to the pipeline [5]
• done by assigning a workspace to the pipeline stage [5]
• the workspace can be assigned to any stage [5]
• {action|optional} make a stage public
• {default} the final stage of the pipeline is made public
• a consumer of a public stage without access to the pipeline sees it as a regular workspace [5]
• without the stage name and deployment pipeline icon on the workspace page next to the workspace name [5]
• {action} deploy to an empty stage
• when finishing the work in one pipeline stage, the content can be deployed to the next stage [5] 
• deployment can happen in any direction [5]
• {option} full deployment 
• deploy all content to the target stage [5]
• {option} selective deployment 
• allows select the content to deploy to the target stage [5]
• {option} backward deployment 
• deploy content from a later stage to an earlier stage in the pipeline [5] 
• {restriction} only possible when the target stage is empty [5]
• {action} deploy content between pages [5]
• content can be deployed even if the next stage has content
• paired items are overwritten [5]
• {action|optional} create deployment rules
• when deploying content between pipeline stages, allow changes to content while keeping some settings intact [5] 
• once a rule is defined or changed, the content must be redeployed
• the deployed content inherits the value defined in the deployment rule [5]
• the value always applies as long as the rule is unchanged and valid [5]
• {feature} deployment history 
• allows to see the last time content was deployed to each stage [5]
• allows to to track time between deployments [5]
• {concept} pairing
• {def} the process by which an item in one stage of the deployment pipeline is associated with the same item in the adjacent stage
• applies to reports, dashboards, semantic models
• paired items appear on the same line in the pipeline content list [5]
• ⇐ items that aren't paired, appear on a line by themselves [5]
• the items remain paired even if their name changes
• items added after the workspace is assigned to a pipeline aren't automatically paired [5]
• ⇐ one can have identical items in adjacent workspaces that aren't paired [5]
• [lakehouse]
• can be removed as a dependent object upon deployment [3]
• supports mapping different Lakehouses within the deployment pipeline context [3]
• {default} a new empty Lakehouse object with same name is created in the target workspace [3]
• ⇐ if nothing is specified during deployment pipeline configuration
• notebook and Spark job definitions are remapped to reference the new lakehouse object in the new workspace [3]
• {warning} a new empty Lakehouse object with same name still is created in the target workspace [3]
• SQL Analytics endpoints and semantic models are provisioned
• no object inside the Lakehouse is overwritten [3]
• updates to Lakehouse name can be synchronized across workspaces in a deployment pipeline context [3] 
• [notebook] deployment rules can be used to customize the behavior of notebooks when deployed [4]
• e.g. change notebook's default lakehouse [4]
• {feature} auto-binding
• binds the default lakehouse and attached environment within the same workspace when deploying to next stage [4]
• [environment] custom pool is not supported in deployment pipeline
• the configurations of Compute section in the destination environment are set with default values [6]
• ⇐ subject to change in upcoming releases [6]
• [warehouse]
• [database project] ALTER TABLE to add a constraint or column
• {limitation} the table will be dropped and recreated when deploying, resulting in data loss
• {recommendation} do not create a Dataflow Gen2 with an output destination to the warehouse
• ⇐ deployment would be blocked by a new item named DataflowsStagingWarehouse that appears in the deployment pipeline [10]
• SQL analytics endpoint is not supported
• [Eventhouse]
• {limitation} the connection must be reconfigured in destination that use Direct Ingestion mode [8]
• [EventStream]
• {limitation} limited support for cross-workspace scenarios
• {recommendation} make sure all EventStream destinations within the same workspace [8]
• KQL database
• applies to tables, functions, materialized views [7]
• KQL queryset
• ⇐ tabs, data sources [7]
• [real-time dashboard]
• data sources, parameters, base queries, tiles [7]
• [SQL database]
• includes the specific differences between the individual database objects in the development and test workspaces [9]
• can be also used with
• Copy Job
• dataflows gen2
• data pipeline
• mirrored database
• activator 
• Power BI items

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References:

[1] Microsoft Learn (2024) Get started with deployment pipelines [link]

[2] Microsoft Learn (2024) Implement continuous integration and continuous delivery (CI/CD) in Microsoft Fabric [link]

[3] Microsoft Learn (2024)  Lakehouse deployment pipelines and git integration (Preview) [link]

[4] Microsoft Learn (2024) Notebook source control and deployment [link] 

[5] Microsoft Learn (2024) Introduction to deployment pipelines [link]

[6] Environment Git integration and deployment pipeline [link]

[7] Microsoft Learn (2024) Microsoft Learn (2024) Real-Time Intelligence: Git integration and deployment pipelines (Preview) [link]

[8] Microsoft Learn (2024) Eventstream CI/CD - Git Integration and Deployment Pipeline [link]

[9] Microsoft Learn (2024) Get started with deployment pipelines integration with SQL database in Microsoft Fabric [link]

[10] Microsoft Learn (2025) Source control with Warehouse (preview) [link] 

Resources:

Acronyms:
CLM - Content Lifecycle Management
UAT - User Acceptance Testing