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Announcing Power Query dataflows for Dataverse in Teams (Preview).

Today we’re excited to announce that you can now use Power Query dataflows (preview) to load data into the web version of Dataverse for Teams in the Brazil region. We are gradually rolling out to other regions and it will be available in all regions by the beginning of July. You can now upload your data from a variety of data sources using the familiar Power Query experience that also ships as part of Excel and Power BI.  The ability to use dataflows in the Teams client will follow soon!

Why bring data into Dataverse for Teams?

Microsoft Dataverse for Teams delivers a built-in, low-code data platform for Microsoft Teams. It provides relational data storage, rich data types, enterprise-grade governance, and one-click solution deployment. Dataverse for Teams enables everyone to easily build and deploy apps.

Before today, the way to get data into Dataverse for Teams was by manually adding data directly into a table. This process can be prone to errors and is not scalable.  But now, with self-service data prep you can find, clean, shape, and import your data into Dataverse for Teams.

With your master data already sitting in a different location, you can use Power Query dataflows to directly access your data through the connectors and load the data into Dataverse for Teams. When data is updated in your master data, you can refresh your dataflows by just one click and the data in Dataverse for Teams is updated too. You can also use the Power Query data transformations to easily validate and clean your data and enforce data quality for your Apps.

What are dataflows?

Dataflows were introduced to help organizations retrieve data from disparate sources and prepare it for consumption. You can easily create dataflows using familiar, self-service Power Query experience to ingest, transform, integrate, and enrich data. When creating a dataflow, you will:

  1. Connect to data.
  2. Transform the data.
  3. Load data into Dataverse for Teams tables.

Once the dataflow is created, it will begin the process of importing data into the dataverse table. Then you can start building apps to leverage that data.

How to create a dataflow in Teams?

There are two places where you can get started creating dataflows in Teams.

Option 1: Sing in to Teams, and then select the link for Power Apps. Select the Build You will see a new sub-tab Dataflows (Preview). Click on the New button to create a new dataflow.  Here you can also manage all your dataflows and create new ones to import data into Dataverse for Teams.

Option 2: Sing in to Teams, and then select the link for Power Apps. Select the Build tab, and then See all. Navigate to the Tables On the Tables tab, choose the Get data button. This button allows you to create dataflows directly from the tables tab. To view/manage/edit your dataflows, you need to go to the Dataflows (Preview) tab explained in Option 1.

Connect to your data source and prepare your data.

You can use dataflows to ingest data from a variety of supported data sources including Excel, CSV, the web, and more. See the documentation for the full list of supported data sources.

After selecting the data source, you can use the Power Query experience to as needed to filter your data. With Power Query you can apply more than 300 different transformations on your data. You can learn more about Power Query here.

When you are finished shaping your data, you can load your data into new Dataverse tables. You can also map your data to tables already created in teams. Learn more about mapping data here.

Once you’ve created and published your dataflow, data will begin loading into Dataverse for Teams. This process can take some time and you can use the management page to check the status. When a dataflow completes a run, its data is available to use.

Managing your dataflows.

You can manage any dataflow you created from the Dataflows (Preview) tab. Here, you can see the status of all dataflows.

Possible states are:

  • Refresh in progress: The dataflow is extracting, transforming, and loading your data from the source to the Dataverse Tables. This process can take several minutes depending on the complexity of transformations and data source’s performance. It is recommended to check the status of the dataflow frequently.
  • In the Last Refresh column, you can see when your data was last refreshed. If your refresh failed, an error indication appears. If you click on the error indication, the details of the error and recommended steps to address it will appear.

You can navigate to the action bar by clicking on the three dots “” next to your dataflow.

Here you can:

  • Edit your dataflow if you want to change your transformation logic or mapping.
  • Rename your dataflow. At creation, an autogenerated name is assigned.
  • Refresh your dataflow. When you refresh your dataflows, the data will be updated.
  • Delete your dataflow.
  • Show refresh history.

When clicking on show refresh history you can see information about the last refresh of your dataflow. When the dataflow refresh is successful, you can see how many rows were added or updated in Dataverse. When your dataflow refresh was not successful, you can investigate why with the help of the error message.

Dataflows in Teams is a lightweight version.

Dataflows for Teams are created to make it easy and fast to load data into Dataverse for Teams tables, so you can start building your apps quickly. It is optimized for one-time import of data without management overhead.

Dataflows in Dataverse for Teams is a lightweight version of dataflows in the Maker Portal and can only load data into Dataverse for Teams. If you want the full functionality of dataflows or make use of analytical dataflows, you can

The use of data sources in Teams does not support a gateway. Supported data sources in Dataflows in Dataverse for Teams are:

  • Excel (OneDrive)
  • Text/CSV (OneDrive)
  • PDF (OneDrive)
  • SharePoint Online
  • SharePoint Online list
  • XML (OneDrive)
  • JSON (OneDrive)
  • OData
  • Web API

For more information on dataflows for Teams, see our documentation.