Merge branch 'main' into trdehove-patch-guidelines-update

Author: sericks007

powerapps-docs/maker/TOC.yml

@@ -1903,8 +1903,12 @@
       href: ./data-platform/create-edit-entities-portal.md
     - name: "Create a custom table that has components"
       href: ./data-platform/create-custom-entity.md
-    - name: Import or export data
-      href: ./data-platform/data-platform-import-export.md
+    - name: Import and export table data
+      items:
+      - name: Import data from Excel and export to CSV
+        href: ./data-platform/data-platform-import-export.md
+      - name: Import data with a dataflow connector
+        href: ./data-platform/create-and-use-dataflows.md     
     - name: Long term data retention for tables
       items:
       - name: Long term data retention overview

powerapps-docs/maker/data-platform/create-and-use-dataflows.md

@@ -2,7 +2,7 @@
 title: "Create and use dataflows in Power Apps | MicrosoftDocs"
 description: "Learn how to create and use dataflows in Power Apps"
 ms.custom: ""
-ms.date: 10/03/2023
+ms.date: 08/06/2024
 ms.reviewer: ""
 ms.suite: ""
 ms.tgt_pltfrm: ""
@@ -32,45 +32,33 @@ A dataflow is a collection of tables that are created and managed in environment
 schedules, directly from the environment in which your dataflow was created.
 
 Once you create a dataflow in the Power Apps portal, you can get data from it
-using the Common Data Service connector or Power BI Desktop Dataflow connector, depending on
+using the Dataverse 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 Power Apps 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.
+1. Author the dataflow in the Power Apps 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.
+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.
+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. 
+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'll 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
+Dataflows are created in one environment. Therefore, you'll 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.
 
 > [!NOTE]
 > Creating dataflows is currently not available with Power Apps Developer Plan licenses.
 
-1.  Sign in to Power Apps, and verify which environment you're in, find the environment switcher near the right side of the command bar.
+1. Sign in to Power Apps, and verify which environment you're in, find the environment switcher near the right side of the command bar.
 
     ![Environment switcher.](media/environment-switcher.png)
 
 1. On the left navigation pane, select **Dataflows**. [!INCLUDE [left-navigation-pane](../../includes/left-navigation-pane.md)]
-1. select **New dataflow**, and then select **Start from blank**.
-1. On the New Dataflow page, enter a **Name** for the dataflow. By default, dataflows store tables in Dataverse. Select **Analytical entities only** if you want tables to be stored in your organization's Azure Data Lake storage account. Select **Create**. 
+1. Select **New dataflow**. On the **New Dataflow** page, enter a **Name** for the dataflow. By default, dataflows store tables in Dataverse. Select **Analytical entities only** if you want tables to be stored in your organization's Azure Data Lake storage account. Select **Create**.
 
      > [!IMPORTANT]
      > There is only one owner of any dataflow—the person who created it. Only the owner can edit the dataflow. Authorization
@@ -95,31 +83,21 @@ 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.](media/choose-data.png)
 
-Now that you've selected the data to use in the table, you can use the dataflow editor to
-shape or transform that data into the format necessary for use in your dataflow.
+Now that you've selected the data to use in the table, 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 table 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](/power-bi/desktop-query-overview).
+You can shape your data selection into a form that works best for your table 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](/power-bi/desktop-query-overview).
 
-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.
+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.](media/advanced-editor.png)
 
 ## Dataflows and the Common Data Model
 
-Dataflows tables 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
-table columns as defined by the Common Data Model. Standard tables have a
-known schema defined by the Common Data Model.
+Dataflows tables include new tools to easily map your business data to the Common Data Model, enrich it with Microsoft and non-Microsoft 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 table columns as defined by the Common Data Model. Standard tables 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](/common-data-model/).
 
@@ -129,83 +107,58 @@ To leverage the Common Data Model with your dataflow, select the **Map to Standa
 
 When you map a source column to a standard column, the following occurs:
 
-1.  The source column takes on the standard column name (the column is renamed if
-    the names are different). 
+1. The source column takes on the standard column name (the column is renamed if
+    the names are different).
 
-2.  The source column gets the standard column data type. 
+2. The source column gets the standard column data type.
 
-To keep the Common Data Model standard table, all standard columns that aren't
-mapped get *Null* values.
+To keep the Common Data Model standard table, all standard columns that aren't mapped get *Null* values.
 
-All source columns that aren't mapped remain as is to ensure that the result
-of the mapping is a standard table with custom columns.
+All source columns that aren't mapped remain as is to ensure that the result of the mapping is a standard table with custom columns.
 
-Once you’ve completed your selections and your table and its data settings are
-complete, you’re ready for the next step, which is selecting the refresh frequency of your
-dataflow.
+Once you’ve completed your selections and your table 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 tables have been defined, you’ll want to schedule the refresh
-frequency for each of your connected data sources.
+Once your tables have been defined, you should 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**.
+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.](media/refresh-automatically.png)
 
 2. Enter the dataflow refresh frequency, start date, and time, in UTC.
 
 3. Select **Create.**
-<!-- 
-## Connect to your dataflow in Power BI Desktop
-Once you’ve created your dataflow and you have scheduled the refresh frequency
-for each data source that will populate the model, you’re ready for the final task, which is connecting to your dataflow from within Power BI Desktop.
-
-To connect to the dataflow, in Power BI Desktop select **Get Data** > **Power Platform** > **Power Platform dataflows** > **Connect**.
-
-![Connect to the dataflow.](media/get-data.png)
 
-Navigate to the environment where you saved your dataflow, select
-the dataflow, and then select the tables that you created from the list.
+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](/power-query/dataflows/connect-azure-data-lake-storage-for-dataflow) 
 
-You can also use the search bar, near the top of the window, to quickly find the
-name of your dataflow or tables from among many dataflow tables.
+## Troubleshooting data connections
 
-When you select the table and then select the **Load** button, the tables
-appear in the **Columns** pane in Power BI Desktop, and appear and behave just
-like tables from any other dataset. -->
+There might be occasions when connecting to data sources for dataflows runs into issues. This section provides troubleshooting tips when issues occur.
 
-## Using dataflows stored in Azure Data Lake Storage Gen2
+- **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.
 
-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](/power-query/dataflows/connect-azure-data-lake-storage-for-dataflow) 
+- **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 `https://microsoft.sharepoint.com/teams/ObjectModel`.
 
-## Troubleshooting data connections
+- **JSON File connector.** Currently you can connect to a JSON file using basic authentication only. For example, a URL similar to `https://XXXXX.blob.core.windows.net/path/file.json?sv=2019-01-01&si=something&sr=c&sig=123456abcdefg` is currently not supported.
 
-There might be occasions when connecting to data sources for dataflows runs into
-issues. This section provides troubleshooting tips when issues occur.
+- **Azure Synapse Analytics.** Dataflows don't currently support Microsoft Entra authentication for Azure Synapse Analytics. Use basic authentication for this scenario.
 
--   **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.
+> [!NOTE]
+> If you use data loss prevention (DLP) policies to block the **HTTP with Microsoft Entra (preauthorized)** connector then **SharePoint** and **OData** connectors will fail. The **HTTP with Microsoft Entra (preauthorized)** connector needs to be allowed in DLP policies for **SharePoint** and **OData** connectors to work.
 
--   **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 `https://microsoft.sharepoint.com/teams/ObjectModel`.
+### Troubleshoot the error: Connection to Dataverse failed. Please check the link below on how to fix this issue
 
+Users might receive an error message if the connection they're using for export requires a fix. In this case, the user receives an error message that states **Connection to Dataverse failed. Please check the link below on how to fix this issue**.
 
--   **JSON File connector.** Currently you can connect to a JSON file using
-    basic authentication only. For example, a URL similar to `https://XXXXX.blob.core.windows.net/path/file.json?sv=2019-01-01&si=something&sr=c&sig=123456abcdefg` is currently not supported.
+To resolve this issue:
 
--   **Azure Synapse Analytics.** Dataflows don't currently support Azure
-    Active Directory authentication for Azure Synapse Analytics. Use
-    basic authentication for this scenario.
+1. In Power Apps (make.powerapps.com), select **Connections** from the left navigation pane. [!INCLUDE [left-navigation-pane](../../includes/left-navigation-pane.md)]
+2. Locate the **Microsoft Dataverse (legacy)** connection.
+3. Select the **Fix connection** link in the **Status** column, and follow the instructions on your screen.
 
-> [!NOTE]
-> If you use data loss prevention (DLP) policies to block the **HTTP with Microsoft Entra (preauthorized)** connector then **SharePoint** and **OData** connectors will fail. The **HTTP with Microsoft Entra (preauthorized)** connector needs to be allowed in DLP policies for **SharePoint** and **OData** connectors to work.
+After the fix completes, retry the export.
 
 ## Next steps