Create and use dataflows in PowerApps

[This topic is pre-release documentation and is subject to change.]

With advanced data preparation available in PowerApps, you can create a collection of data called a dataflow, which you can then use to connect with business data from various sources, clean the data, transform it, and then load it to Common Data Service or your organization’s Azure Data Lake Gen2 storage account.

A dataflow is a collection of entities (entities are similar to tables) that are created and managed in environments in the PowerApps service. You can add and edit entities in your dataflow, as well as manage data refresh schedules, directly from the environment in which your dataflow was created.

Once you create a dataflow in the PowerApps portal, you can get data from it using the Common Data Service connector or Power BI Desktop Dataflow connector, depending on which destination you chose when creating the dataflow.

There are three primary steps to using a dataflow:

  1. Author the dataflow in the PowerApps portal. You select the destination to load the output data to, the source to get the data from, and the Power Query steps to transform the data using Microsoft tools that are designed to make doing so straightforward.

  2. Schedule dataflow runs. This is the frequency in which the Power Platform Dataflow should refresh the data that your dataflow will load and transform.

  3. Use the data you loaded to the destination storage. You can build apps, flows, Power BI reports, and dashboards or connect directly to the dataflow’s Common Data Model folder in your organization’s lake using Azure data services like Azure Data Factory, Azure Databricks or any other service that supports the Common Data Model folder standard.

The following sections look at each of these steps so you can become familiar with the tools provided to complete each step.

Create a dataflow

Dataflows are created in one environment. Therefore, you will only be able to see and manage them from that environment. In addition, individuals who want to get data from your dataflow must have access to the environment in which you created it.


Creating Dataflows that load data to Azure Data Lake Storage Gen2 in the default environment is currently not supported.

  1. Sign in to PowerApps, and verify which environment you're in, find the environment switcher near the right side of the command bar.

    Environment switcher

  2. On the left navigation pane, select the down arrow next to Data.

    Data select

  3. In the Data list, select Dataflows and then select New dataflow.

    Create a dataflow

  4. On the Select load target page, select the destination storage where you want entities to be stored. Dataflows can store entities in Common Data Service or in your organization's Azure Data Lake storage account. Once you select a destination to load data to, enter a Name for the dataflow, and then select Create.

    Select load target


    There is only one owner of any dataflow—the person who created it. Only the owner can edit the dataflow. Authorization and access to data created by the dataflow depend on the destination you loaded data to. Data loaded into Common Data Service will be available via the Common Data Service Connector and requires the person accessing the data to be authorized to Common Data Service. Data loaded into your organization’s Azure Data Lake Gen2 storage account is accessible via the Power Platform Dataflow connector and access to it requires membership within the environment it was created in.

  5. On the Choose data source page, select the data source where the entities are stored, and then select Create. The selection of data sources displayed allows you to create dataflow entities.

    Choose a data source

  6. After you select a data source, you’re prompted to provide the connection settings, including the account to use when connecting to the data source.

    Connect to data source

  7. Once connected, you select the data to use for your entity. When you choose data and a source, the Power Platform Dataflow service will subsequently reconnect to the data source in order to keep the data in your dataflow refreshed, at the frequency you select later in the setup process.

    Choose data

Now that you've selected the data to use in the entity, you can use the dataflow editor to shape or transform that data into the format necessary for use in your dataflow.

Use the dataflow editor to shape or transform data

You can shape your data selection into a form that works best for your entity using a Power Query editing experience, similar to the Power Query Editor in Power BI Desktop. To learn more about Power Query, see Query overview in Power BI Desktop.

If you want to see the code that Query Editor is creating with each step, or if you want to create your own shaping code, you can use the advanced editor.

Advanced editor

Dataflows and the Common Data Model

Dataflows entities include new tools to easily map your business data to the Common Data Model, enrich it with Microsoft and third-party data, and gain simplified access to machine learning. These new capabilities can be leveraged to provide intelligent and actionable insights into your business data. Once you’ve completed any transformations in the edit queries step described below, you can map columns from your data source tables to standard entity fields as defined by the Common Data Model. Standard entities have a known schema defined by the Common Data Model.

For more information about this approach, and about the Common Data Model, see The Common Data Model.

To leverage the Common Data Model with your dataflow, select the Map to Standard transformation in the Edit Queries dialog. In the Map Entities screen that appears, select the standard entity that you want to map.

Map to standard entity

When you map a source column to a standard field, the following occurs:

  1. The source column takes on the standard field name (the column is renamed if the names are different).

  2. The source column gets the standard field data type.

To keep the Common Data Model standard entity, all standard fields that are not mapped get Null values.

All source columns that are not mapped remain as is to ensure that the result of the mapping is a standard entity with custom fields.

Once you’ve completed your selections and your entity and its data settings are complete, you’re ready for the next step, which is selecting the refresh frequency of your dataflow.

Set the refresh frequency

Once your entities have been defined, you’ll want to schedule the refresh frequency for each of your connected data sources.

  1. Dataflows use a data refresh process to keep data up to date. In the Power Platform Dataflow authoring tool, you can choose to refresh your dataflow manually or automatically on a scheduled interval of your choice. To schedule a refresh automatically, select Refresh automatically.

    Refresh automatically

  2. Enter the dataflow refresh frequency, start date, and time, in UTC.

  3. Select Create.

Using dataflows stored in Azure Data Lake Storage Gen2

Some organizations might want to use their own storage for creation and management of dataflows. You can integrate dataflows with Azure Data Lake Storage Gen2 if you follow the requirements to set up the storage account properly. More information: Connect Azure Data Lake Storage Gen2 for dataflow storage

Troubleshooting data connections

There might be occasions when connecting to data sources for dataflows runs into issues. This section provides troubleshooting tips when issues occur.

  • Salesforce connector. Using a trial account for Salesforce with dataflows results in a connection failure with no information provided. To resolve this, use a production Salesforce account or a developer account for testing.

  • SharePoint connector. Make sure you supply the root address of the SharePoint site, without any subfolders or documents. For example, use a link similar to

  • JSON File connector. Currently you can connect to a JSON file using basic authentication only. For example, a URL similar to is currently not supported.

  • Azure SQL Data Warehouse. Dataflows do not currently support Azure Active Directory authentication for Azure SQL Data Warehouse. Use basic authentication for this scenario.

Next steps

The following articles are useful for further information and scenarios when using dataflows:

For more information about the Common Data Model: