Article tweaks

markingmyname · markingmyname · commit 5044b04d719a · 2020-05-21T18:24:59.000-05:00
diff --git a/articles/data-factory/author-visually.md b/articles/data-factory/author-visually.md
@@ -26,7 +26,7 @@ To open the **authoring canvas**, click on the pencil icon.
 
 ![Authoring Canvas](media/author-visually/authoring-canvas.png)
 
-Here, you will author the pipelines, activities, datasets, linked services, data flows, triggers, and integration runtimes that comprise your factory. To get started building a pipeline using the authoring canvas, see [Copy data using the copy Activity](tutorial-copy-data-portal.md). 
+Here, you author the pipelines, activities, datasets, linked services, data flows, triggers, and integration runtimes that comprise your factory. To get started building a pipeline using the authoring canvas, see [Copy data using the copy Activity](tutorial-copy-data-portal.md). 
 
 The default visual authoring experience is directly working with the Data Factory service. Azure Repos Git or GitHub integration is also supported to allow source control and collaboration for work on your data factory pipelines. To learn more about the differences between these authoring experiences, see [Source control in Azure Data Factory](source-control.md).
 
@@ -36,7 +36,7 @@ For top-level resources such as pipelines, datasets, and data flows, high-level
 
 ![Authoring Canvas](media/author-visually/properties-pane.png)
 
-The properties pane will only be open by default on resource creation. To edit it, click on the properties pane icon located in the top-right corner of the canvas.
+The properties pane only opens by default on resource creation. To edit it, click on the properties pane icon located in the top-right corner of the canvas.
 
 ## Expressions and functions
 
diff --git a/articles/data-factory/concepts-datasets-linked-services.md b/articles/data-factory/concepts-datasets-linked-services.md
@@ -29,9 +29,9 @@ If you are new to Data Factory, see [Introduction to Azure Data Factory](introdu
 ## Overview
 A data factory can have one or more pipelines. A **pipeline** is a logical grouping of **activities** that together perform a task. The activities in a pipeline define actions to perform on your data. Now, a **dataset** is a named view of data that simply points or references the data you want to use in your **activities** as inputs and outputs. Datasets identify data within different data stores, such as tables, files, folders, and documents. For example, an Azure Blob dataset specifies the blob container and folder in Blob storage from which the activity should read the data.
 
-Before you create a dataset, you must create a [**linked service**](concepts-linked-services.md) to link your data store to the data factory. Linked services are much like connection strings, which define the connection information needed for Data Factory to connect to external resources. Think of it this way; the dataset represents the structure of the data within the linked data stores, and the linked service defines the connection to the data source. For example, an Azure Storage linked service links a storage account to the data factory. An Azure Blob dataset represents the blob container and the folder within that Azure storage account that contains the input blobs to be processed.
+Before you create a dataset, you must create a [**linked service**](concepts-linked-services.md) to link your data store to the data factory. Linked services are much like connection strings, which define the connection information needed for Data Factory to connect to external resources. Think of it this way; the dataset represents the structure of the data within the linked data stores, and the linked service defines the connection to the data source. For example, an Azure Storage linked service links a storage account to the data factory. An Azure Blob dataset represents the blob container and the folder within that Azure Storage account that contains the input blobs to be processed.
 
-Here is a sample scenario. To copy data from Blob storage to a SQL database, you create two linked services: Azure Storage and Azure SQL Database. Then, create two datasets: Azure Blob dataset (which refers to the Azure Storage linked service) and Azure SQL Table dataset (which refers to the Azure SQL Database linked service). The Azure Storage and Azure SQL Database linked services contain connection strings that Data Factory uses at runtime to connect to your Azure Storage and Azure SQL Database, respectively. The Azure Blob dataset specifies the blob container and blob folder that contains the input blobs in your Blob storage. The Azure SQL Table dataset specifies the SQL table in your SQL database to which the data is to be copied.
+Here is a sample scenario. To copy data from Blob storage to a SQL Database, you create two linked services: Azure Storage and Azure SQL Database. Then, create two datasets: Azure Blob dataset (which refers to the Azure Storage linked service) and Azure SQL Table dataset (which refers to the Azure SQL Database linked service). The Azure Storage and Azure SQL Database linked services contain connection strings that Data Factory uses at runtime to connect to your Azure Storage and Azure SQL Database, respectively. The Azure Blob dataset specifies the blob container and blob folder that contains the input blobs in your Blob storage. The Azure SQL Table dataset specifies the SQL table in your SQL Database to which the data is to be copied.
 
 The following diagram shows the relationships among pipeline, activity, dataset, and linked service in Data Factory:
 
@@ -119,7 +119,7 @@ typeProperties | The type properties are different for each type (for example: A
 
 
 ## Dataset example
-In the following example, the dataset represents a table named MyTable in a SQL database.
+In the following example, the dataset represents a table named MyTable in a SQL Database.
 
 ```json
 {
@@ -202,7 +202,7 @@ Define the Blob dataset structure as follows along with type definitions for the
 The following guidelines help you understand when to include structure information, and what to include in the **structure** section. Learn more on how data factory maps source data to sink and when to specify structure information from [Schema and type mapping](copy-activity-schema-and-type-mapping.md).
 
 - **For strong schema data sources**, specify the structure section only if you want map source columns to sink columns, and their names are not the same. This kind of structured data source stores data schema and type information along with the data itself. Examples of structured data sources include SQL Server, Oracle, and Azure SQL Database.<br/><br/>As type information is already available for structured data sources, you should not include type information when you do include the structure section.
-- **For no/weak schema data sources e.g. text file in blob storage**, include structure when the dataset is an input for a copy activity, and data types of source dataset should be converted to native types for the sink. And include structure when you want to map source columns to sink columns..
+- **For no/weak schema data sources for example, text file in blob storage**, include structure when the dataset is an input for a copy activity, and data types of source dataset should be converted to native types for the sink. And include structure when you want to map source columns to sink columns
 
 ## Create datasets
 You can create datasets by using one of these tools or SDKs: [.NET API](quickstart-create-data-factory-dot-net.md), [PowerShell](quickstart-create-data-factory-powershell.md), [REST API](quickstart-create-data-factory-rest-api.md), Azure Resource Manager Template, and Azure portal
diff --git a/articles/data-factory/concepts-linked-services.md b/articles/data-factory/concepts-linked-services.md
@@ -15,15 +15,15 @@ ms.date: 04/25/2019
 
 # Linked services in Azure Data Factory
 
-> [!div class="op_single_selector" title1="Select the version of Data Factory service you are using:"]
+> [!div class="op_single_selector" title1="Select the version of Data Factory service you're using:"]
 > * [Version 1](v1/data-factory-create-datasets.md)
 > * [Current version](concepts-linked-services.md)
 
 [!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
 This article describes what linked services are, how they're defined in JSON format, and how they're used in Azure Data Factory pipelines.
 
-If you are new to Data Factory, see [Introduction to Azure Data Factory](introduction.md) for an overview.
+If you're new to Data Factory, see [Introduction to Azure Data Factory](introduction.md) for an overview.
 
 ## Overview
 
diff --git a/articles/data-factory/concepts-roles-permissions.md b/articles/data-factory/concepts-roles-permissions.md
@@ -7,7 +7,6 @@ ms.service: data-factory
 services: data-factory
 documentationcenter: ''
 ms.workload: data-services
-
 author: djpmsft
 ms.author: daperlov
 manager: anandsub
@@ -65,7 +64,7 @@ Here are a few examples that demonstrate what you can achieve with custom roles:
 
 - Let a user create, edit, or delete any data factory in a resource group from the Azure portal.
 
-  Assign the built-in **Data Factory contributor** role at the resource group level for the user. If you want to allow access to any data factory in a subscription, assign the role at the subscription level.
+  Assign the built-in **Data Factory contributor** role at the resource group level for the user. If you want to allow,  access to any data factory in a subscription, assign the role at the subscription level.
 
 - Let a user view (read) and monitor a data factory, but not edit or change it.
 
@@ -80,7 +79,7 @@ Here are a few examples that demonstrate what you can achieve with custom roles:
 
 - Let a user only be able to test connection in a linked service
 
-    Create a custom role role with permissions for the following actions: **Microsoft.DataFactory/factories/getFeatureValue/read** and **Microsoft.DataFactory/factories/getDataPlaneAccess/read**. Assign this custom role on the data factory resource for the user.
+    Create a custom role with permissions for the following actions: **Microsoft.DataFactory/factories/getFeatureValue/read** and **Microsoft.DataFactory/factories/getDataPlaneAccess/read**. Assign this custom role on the data factory resource for the user.
 
 - Let a user update a data factory from PowerShell or the SDK, but not in the Azure portal.
 
diff --git a/articles/data-factory/control-flow-set-variable-activity.md b/articles/data-factory/control-flow-set-variable-activity.md
@@ -24,8 +24,8 @@ Property | Description | Required
 name | Name of the activity in pipeline | yes
 description | Text describing what the activity does | no
 type | Must be set to **SetVariable** | yes
-value | String literal or expression object value that the variable will be assigned to | yes
-variableName | Name of the variable that will be set by this activity | yes
+value | String literal or expression object value that the variable is assigned to | yes
+variableName | Name of the variable that is set by this activity | yes
 
 ## Incrementing a variable
 
diff --git a/articles/data-factory/v1/data-factory-build-your-first-pipeline-using-powershell.md b/articles/data-factory/v1/data-factory-build-your-first-pipeline-using-powershell.md
@@ -20,8 +20,6 @@ ms.date: 01/22/2018
 > * [PowerShell](data-factory-build-your-first-pipeline-using-powershell.md)
 > * [Resource Manager Template](data-factory-build-your-first-pipeline-using-arm.md)
 > * [REST API](data-factory-build-your-first-pipeline-using-rest-api.md)
->
->
 
 
 > [!NOTE]
@@ -112,7 +110,7 @@ In this step, you link your Azure Storage account to your data factory. You use
         }
     }
     ```
-    Replace **account name** with the name of your Azure storage account and **account key** with the access key of the Azure storage account. To learn how to get your storage access key, see [Manage storage account access keys](../../storage/common/storage-account-keys-manage.md).
+    Replace **account name** with the name of your Azure Storage account and **account key** with the access key of the Azure Storage account. To learn how to get your storage access key, see [Manage storage account access keys](../../storage/common/storage-account-keys-manage.md).
 2. In Azure PowerShell, switch to the ADFGetStarted folder.
 3. You can use the **New-AzDataFactoryLinkedService** cmdlet that creates a linked service. This cmdlet and other Data Factory cmdlets you use in this tutorial requires you to pass values for the *ResourceGroupName* and *DataFactoryName* parameters. Alternatively, you can use **Get-AzDataFactory** to get a **DataFactory** object and pass the object without typing *ResourceGroupName* and *DataFactoryName* each time you run a cmdlet. Run the following command to assign the output of the **Get-AzDataFactory** cmdlet to a **$df** variable.
 
@@ -310,7 +308,7 @@ In this step, you create your first pipeline with a **HDInsightHive** activity.
     ```
     In the JSON snippet, you are creating a pipeline that consists of a single activity that uses Hive to process Data on an HDInsight cluster.
 
-    The Hive script file, **partitionweblogs.hql**, is stored in the Azure storage account (specified by the scriptLinkedService, called **StorageLinkedService**), and in **script** folder in the container **adfgetstarted**.
+    The Hive script file, **partitionweblogs.hql**, is stored in the Azure Storage account (specified by the scriptLinkedService, called **StorageLinkedService**), and in **script** folder in the container **adfgetstarted**.
 
     The **defines** section is used to specify the runtime settings that be passed to the hive script as Hive configuration values (e.g ${hiveconf:inputtable}, ${hiveconf:partitionedtable}).
 
