Revised for style

Author: Mattp123

powerapps-docs/maker/data-platform/export-to-data-lake-data-adf.md

@@ -2,7 +2,7 @@
 title: "Ingest Microsoft Dataverse data with Azure Data Factory | MicrosoftDocs"
 description: Learn how to use Azure Data Factory to create dataflows, transform, and run analysis on Dataverse data
 ms.custom: ""
-ms.date: 07/29/2020
+ms.date: 03/22/2021
 ms.reviewer: "matp"
 author: sabinn-msft
 ms.service: powerapps
@@ -49,20 +49,19 @@ The user account that's used to sign in to Azure must be a member of the
 To view the permissions that you have in the subscription, go to the [Azure portal](https://portal.azure.com/), select your username in the upper-right corner, select **...**, and then select **My permissions**. If you have access to multiple subscriptions, select the appropriate one. To create and manage child resources for Data Factory in the Azure portal—including datasets, linked services, pipelines, triggers, and integration runtimes—you must belong to the *Data Factory Contributor* role at the resource group level or above.
 
 ### Export to data lake
-
-This guide assumes that you have already exported Dataverse data by using the [Export to Data Lake service](export-to-data-lake.md).
+This guide assumes that you've already exported Dataverse data by using the [Export to Data Lake service](export-to-data-lake.md).
 
 In this example, account table data is exported to the data lake.
 
 ### Azure Data Factory
 
-This guide assumes that you have already created a data factory under the same subscription and resource group as the storage account containing the exported Dataverse data.
+This guide assumes that you've already created a data factory under the same subscription and resource group as the storage account containing the exported Dataverse data.
 
 ## Set the Data Lake Storage Gen2 storage account as a source
 
-1.  Open [Azure Data Factory](https://ms-adf.azure.com/en-us/datafactories) and select the data facotry that is on the same subscription and resource group as the storage account containing your exported Dataverse data. Then select **Create data flow** from the home page. 
+1.  Open [Azure Data Factory](https://ms-adf.azure.com/en-us/datafactories) and select the data factory that is on the same subscription and resource group as the storage account containing your exported Dataverse data. Then select **Create data flow** from the home page. 
 
-2.  Turn on **Data flow debug** mode and select your preferred time to live. This may take up to 10 minutes, but you
+2.  Turn on **Data flow debug** mode and select your preferred time to live. This might take up to 10 minutes, but you
     can proceed with the following steps.
 
     ![Dataflow debug mode](media/data-flow-debug.png "Dataflow debug mode")
@@ -71,7 +70,7 @@ This guide assumes that you have already created a data factory under the same s
 
     ![Add source](media/add-source.png "Add source")
 
-4.  Under **Source settings**, do the following<!--Suggested. It's "configure the following options" here and "select the following options" in the next procedure, but these are a combination of entering and selecting.-->:
+4.  Under **Source settings**, do the following:
 
     - **Output stream name**: Enter the name you want. 
     - **Source type**: Select **Common Data Model**.
@@ -88,7 +87,7 @@ This guide assumes that you have already created a data factory under the same s
 
 6.  Check the **Projection** tab to ensure that your schema has been imported sucessfully. If you do not see any columns, select **Schema options** and check the **Infer drifted column types** option. Configure the formatting options to match your data set then select **Apply**.
 
-7. You may view your data in the **Data preview** tab to ensure the Source creation was complete and accurate.
+7. You can view your data in the **Data preview** tab to ensure the Source creation was complete and accurate.
 
 ## Transform your Dataverse data
 After setting the exported Dataverse data in the Data Lake Storage Gen2 storage account as a source in the Data Factory dataflow, there are many possibilities for transforming your data. More information: [Azure Data Factory](/azure/data-factory/introduction)
@@ -106,7 +105,7 @@ Follow these instructions to create a rank for the each row by the *revenue* of
 
        ![Configure the Rank settings tab](media/configure-rank.png "Configure the Rank settings tab")
 
-3. You may view your data in the **data preview** tab where you will find the new *revenueRank* column at the right-most position.
+3. You can view your data in the **data preview** tab where you will find the new *revenueRank* column at the right-most position.
 
 ## Set the Data Lake Storage Gen2 storage account as a sink
 Ultimately, you must set a sink for your dataflow. Follow these instructions to place your transformed data as a Delimited Text file in the Data Lake.
@@ -133,7 +132,7 @@ Ultimately, you must set a sink for your dataflow. Follow these instructions to
 
 3. On the **Optimize** tab, set the **Partition option** to **Single partition**.
 
-4. You may view your data in the **data preview** tab. 
+4. You can view your data in the **data preview** tab. 
 
 ## Run your dataflow
 
@@ -148,7 +147,7 @@ Ultimately, you must set a sink for your dataflow. Follow these instructions to
 
 4.  Select **Debug** from the command bar.
 
-5.  Let the dataflow run until the bottom view shows that is has been completed. This may take a few minutes.
+5.  Let the dataflow run until the bottom view shows that is has been completed. This might take a few minutes.
 
 6.  Go to the final destination storage container, and find the transformed table data file.