POWER BI DEPLOYMENT PIPELINES

Application lifecycle management & staging in Power BI; deploy content, view deployment history & set up a mature Power BI ecosystem.

DATA GOBLINS POWER BI DEPLOYMENT PIPELINE CHECKLIST

Version X.X - Update-in-progress: March 2024


If you are setting up a Deployment Pipeline for the first time, or auditing an existing one, the below checklist helps you figure out what to consider:

Click the question mark (?) for a link to a reference document. This will be learn.microsoft.com documentation; if none are available, this may be a Data Goblins article, or another blog post reference.

Planning

Deployment Pipeline planning & decision-making; this is always done.



Basic Setup

Initial steps always required to create and configure a Deployment Pipeline; mandatory items.



Advanced Setup

Additional configuration sometimes done to facilitate more advanced Application Lifecycle Management (ALM) options.




Monitoring

Optional steps taken to monitor Deployment activity & history.




DEPLOYMENT PIPELINE DOCUMENTATION CHECKLIST

Documentation for Deployment Pipelines should mostly be focused on the process & way-of-working; it should be connected to the team's overall DataOps methodology. A Deployment Pipeline brings no value if the team, for example, deploys directly to Production for "hot fixes".

Deployment Pipeline Documentation


FOOTNOTES

[1] For example, approval of the deployment after peer-review, or objective test criteria.

[2] Apps are not handled by Deployment Pipelines; an App created in Development will not deploy to Test or Production. It may still be meaningful to use Development to create the app, and Test to test the app. The Test App can also be used for UAT.

[3] For example, that content is not deployed directly to Test or Production, content is not edited directly in the Workspaces, and XMLA external tools do not write to Test or Production datasets. The way of working is the most important thing to define; if this is not aligned among team members, the Deployment Pipeline will bring little value.

[4] A common use-case is when data content (Data Marts, Dataflows, Datasets) are in separate Workspaces & Pipelines than reporting content (Reports, Paginated Reports, Dashboards, Metrics). Linking these pipelines is done i.e. with Azure DevOps and the REST API.

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