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How to Share Power BI Datasets: Dataflows and Certified Datasets

February 06, 2020

Microsoft Power BI

Enable Data Transformations and Enterprise Sharing of Datasets

In this on-demand webinar, learn how to easily and wisely share datasets created in Power BI Desktop. We tell you about Power BI dataflows and certified datasets, the time-saving functions that allow analysts to share their valuable work with other report writers.

READ THE ANSWERS TO THE QUESTIONS ASKED DURING THE WEBINAR

With shared datasets, a single dataset can be used by multiple reports, across workspaces. Learning from a Power BI power user, you learn how Power BI dataflows and certified datasets work, why they exist, and how they are going to improve your business intelligence reporting performance and overall enterprise analytics.

Until recently, analysts working in Power BI Desktop could not easily extend their datasets for use by other report writers. Those datasets could only be used and accessed through an analyst’s workspace and apps. To share the dataset, there were two options: one was to perform a do over, cleansing and transforming the data. The other option was an enterprise-level data cleansing and transformation process. Thankfully, Microsoft provided data flows, shared datasets and certified datasets as an in-between solution.

By allowing Power BI administrators to tag datasets as certified, Power BI analysts take advantage of someone else’s data enrichment work with full confidence that the dataset has been vetted by the organization. Dataflows make the process of data preparation—and creation of datasets—more manageable.

If you’re using Power BI Desktop, shared and certified datasets enable an effective data culture. Learn how to use them to quickly build reports, make decisions on trusted data and remix to create new insights.

▼ PRESENTER

Andrew Kinnier
BI Solution Architect
Senturus, Inc.

A certified Microsoft business intelligence architect and developer with over 20 years of experience in software development, Andrew has made regular appearances at the Power Platform World Tour events over the years. He also serves as assistant organizer of the NJ/NY branch of the Power BI User Groups.

▼ TECHNOLOGIES COVERED

Microsoft Power BI

▼ PRESENTATION OUTLINE

  • Enterprise level BI
  • A cube is a cube is a cube (or a dataset)
  • Dataflows – bridge the gap between enterprise and self-service Power BI
    • Power Query (Query Editor) in the cloud and separated from Power BI
    • Dataflows extract data from the sources and persist (save) data in the cloud–behind the scenes it is storing the data in Azure data lake gen2
    • Common data model
    • Demo
    • Pro and Premium
  • Shared datasets
    • Allows datasets to be used in Power BI Desktop and published to a different workspace
    • The dataset is not recreated in the new workspace, it has a link in the new workspace that points back to the published dataset in the other workspace
    • Advantage
      • No more recreating the same dataset and publishing up your Power BI Service environment
      • Saves on space and compute power
    • Demo
  • Certified datasets
    • Gives the organization a way to stamp approval at multiple levels
      • Default – it could be in development, untested, etc., use at your own risk
      • Promoted – anyone can promote their datasets – it is signaling to other users that the creator of the dataset has tested and completed the dataset – you can use it with some comfort level
      • Certified–only certified administrators can attach this level to a dataset – tells the user that the organization has tested and QA’d the dataset with accordance to company business rules
    • Planning
      • Administrator roles have expanded
        • An organization can allow an administrator to certify a dataset
        • The person needs to have some Power BI background and an understanding of the organization’s data to be able to give a stamp of approval, choose wisely
      • Dataflow developers
        • Identifying the Power BI Power users
        • Reusing their work in dataflow development
      • Workspaces
        • Have separate workspaces for dataflows
        • Allow shared workspaces across the organization
      • Training
        • Train power users and data consumers on the distinctions of endorsement