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Evaluating Power BI’s Deployment Pipelines

August 27, 2020

Will they accelerate the deployment of datasets, dashboards and reports in your organization?

Power BI deployment pipelines promise to streamline much of the time consuming, often error-prone lifecycle process currently associated with deploying datasets, reports and dashboards. Placing associated development, testing and production workspaces into a single pipeline and using a clean drag-and-drop UI, the feature simplifies and synchronizes the deployment workflow.

It has some limitations. But for the right use cases, deployment pipelines will turbocharge the ability of your team to keep up with new data and confidently meet the demands from lines of business for new insights.

In this on-demand webinar get our frank assessment of deployment pipelines. We demo the feature, discuss capabilities and limitations and offer recommendations of how to make best use of it.

▼ PRESENTER

Stephen Wullschleger
Vice President
Senturus, Inc.

Steve’s background in BI, operations and finance spans 30 years. During that time he has led numerous business analytics initiatives using Microsoft data tools and has watched the platform evolve from his initial use of version 1 of SQL Server and Microsoft Access. He brings a deep familiarity with the various capabilities and components of the Microsoft BI platform and understanding of how to best leverage them.

▼ TECHNOLOGIES

Power BI Premium

▼ PRESENTATION OUTLINE

  • What is deployment pipelines?
    • According to Microsoft: “An efficient and reusable tool that enables BI creators in an enterprise with Premium capacity to manage the lifecycle of organizational content.
    • Allows developing and testing Power BI content such as reports, dashboards and datasets, before they’re consumed by end users.”
  • Workflow benefits
    • Add the ability to manage content lifecycle (finally)
    • Help build an efficient and reusable process for development, testing and production
    • Allow for improved productivity, faster delivery and reduced user errors
  • Demo and discussion
  • Requirements
    • All in Premium (A, EM, P)
    • Workspace member to view content
    • Permission to create workspaces (if new) or workspaces created for you
    • Workspaces have new workspace experience enabled
  • Permissions
    • Pipeline permissions and workspace permissions are granted and managed separately
    • Users with pipeline access have the following permissions:
      • View the pipeline
      • Share the pipeline with others
      • Edit and delete the pipeline
    • Pipeline access doesn’t grant permissions to view or take actions on the workspace content
  • Suggestions
    • Development workspace permissions = developers
    • Test workspace permissions = limited
    • Production workspace permissions = extremely limited
    • Pipeline permissions = deployment permissions
    • App permissions = viewers
    • Build permissions set in app or at dataset
  • Use cases
    • Include
      • Full deployment
      • Selective deployment
      • Backwards deployment
    • However…
      • Limitations
      • Uses premium resources whereas a shared workspace doesn’t have to
      • No method to revert to previous version
  • Limitations
    • Premium workspaces required for all environments
    • Incremental refresh not supported
    • Sensitivity labels not supported
    • Workspace deployment limited to less than 300 items
    • Template apps not supported
    • Paginated reports
    • Dataflows not supported until Mar 2021
    • Downloading dataset from target workspace not supported
    • Limited data source rules
    • App contents and settings are not copied
    • Assign 1 workspace for 1 deployment pipeline ONLY
    • No rollback/versioning
    • Cannot assign all three to existing workspaces
    • No approvals and notes – does not support compliance standards
  • Best practices
    • Treat each workspace as a complete package of analytics
    • Plan your permission model
    • Connect different stages to different databases
    • Use parameters in your model
    • Development
      • Use Power BI Desktop to edit your reports and datasets
      • Version control for PBLX files
      • Separate modeling development from report and dashboard development
      • Manage your models using XMLA read/write capabilities
    • Test
      • Simulate your production environment
      • Use dataset rules with a real-life data source
      • Measure performance
      • Check related items
      • Test your app
    • Production
      • Manage who can deploy to production
      • Set rules to ensure production stage availability
      • Update the production app
      • Quick fixes to content
    • Next release schedule for March 2021
    • Other approaches
      • GitHub
      • SharePoint/OneDrive
      • Share in the chat
    • Components of overall Power BI administration
      • Center of excellence
      • Tenant and premium administration
      • Premium capacity
      • Performance and use monitoring and optimization
      • Governance
      • Adoption