Merge pull request #97509 from djpmsft/docUpdates

Responding to Github issues

Author: megvanhuygen

articles/data-factory/source-control.md

@@ -5,7 +5,7 @@ services: data-factory
 documentationcenter: ''
 ms.service: data-factory
 ms.workload: data-services
-ms.tgt_pltfrm: na
+ms.tgt_pltfrm: naF
 ms.topic: conceptual
 ms.date: 01/09/2019
 author: djpmsft
@@ -139,7 +139,7 @@ The configuration pane shows the following GitHub repository settings:
 | **GitHub Enterprise URL** | The GitHub Enterprise root URL. For example: https://github.mydomain.com. Required only if **Use GitHub Enterprise** is selected | `<your GitHub enterprise url>` |                                                           
 | **GitHub account** | Your GitHub account name. This name can be found from https:\//github.com/{account name}/{repository name}. Navigating to this page prompts you to enter GitHub OAuth credentials to your GitHub account. | `<your GitHub account name>` |
 | **Repository Name**  | Your GitHub code repository name. GitHub accounts contain Git repositories to manage your source code. You can create a new repository or use an existing repository that's already in your account. | `<your repository name>` |
-| **Collaboration branch** | Your GitHub collaboration branch that is used for publishing. By default, its master. Change this setting in case you want to publish resources from another branch. | `<your collaboration branch>` |
+| **Collaboration branch** | Your GitHub collaboration branch that is used for publishing. By default, it's master. Change this setting in case you want to publish resources from another branch. | `<your collaboration branch>` |
 | **Root folder** | Your root folder in your GitHub collaboration branch. |`<your root folder name>` |
 | **Import existing Data Factory resources to repository** | Specifies whether to import existing data factory resources from the UX authoring canvas into a GitHub repository. Select the box to import your data factory resources into the associated Git repository in JSON format. This action exports each resource individually (that is, the linked services and datasets are exported into separate JSONs). When this box isn't selected, the existing resources aren't imported. | Selected (default) |
 | **Branch to import resource into** | Specifies into which branch the data factory resources (pipelines, datasets, linked services etc.) are imported. You can import resources into one of the following branches: a. Collaboration b. Create new c. Use Existing |  |
@@ -230,7 +230,7 @@ It's recommended to not allow direct check-ins to the collaboration branch. This
 
 ### Using passwords from Azure Key Vault
 
-its recommended to use Azure Key Vault to store any connection strings or passwords for Data Factory Linked Services. For security reasons, we don’t store any such secret information in Git, so any changes to Linked Services are published immediately to the Azure Data Factory service.
+It's recommended to use Azure Key Vault to store any connection strings or passwords for Data Factory Linked Services. For security reasons, we don’t store any such secret information in Git, so any changes to Linked Services are published immediately to the Azure Data Factory service.
 
 Using Key Vault also makes continuous integration and deployment easier as you will not have to provide these secrets during Resource Manager template deployment.
 

articles/data-factory/transform-data.md

@@ -90,7 +90,7 @@ The Azure Databricks Python Activity in a Data Factory pipeline runs a Python fi
 ### Custom activity
 If you need to transform data in a way that is not supported by Data Factory, you can create a custom activity with your own data processing logic and use the activity in the pipeline. You can configure the custom .NET activity to run using either an Azure Batch service or an Azure HDInsight cluster. See [Use custom activities](transform-data-using-dotnet-custom-activity.md) article for details. 
 
-You can create a custom activity to run R scripts on your HDInsight cluster with R installed. See [Run R Script using Azure Data Factory](https://github.com/Azure/Azure-DataFactory/tree/master/Samples/RunRScriptUsingADFSample). 
+You can create a custom activity to run R scripts on your HDInsight cluster with R installed. See [Run R Script using Azure Data Factory](https://github.com/Azure/Azure-DataFactory/tree/master/SamplesV1/RunRScriptUsingADFSample). 
 
 ### Compute environments
 You create a linked service for the compute environment and then use the linked service when defining a transformation activity. There are two types of compute environments supported by Data Factory. 

articles/data-factory/v1/data-factory-azure-blob-connector.md

@@ -46,7 +46,7 @@ You can copy data from the following data stores **to Azure Blob Storage**:
 > [!IMPORTANT]
 > Copy Activity supports copying data from/to both general-purpose Azure Storage accounts and Hot/Cool Blob storage. The activity supports **reading from block, append, or page blobs**, but supports **writing to only block blobs**. Azure Premium Storage is not supported as a sink because it is backed by page blobs.
 >
-> Copy Activity does not delete data from the source after the data is successfully copied to the destination. If you need to delete source data after a successful copy, create a [custom activity](data-factory-use-custom-activities.md) to delete the data and use the activity in the pipeline. For an example, see the [Delete blob or folder sample on GitHub](https://github.com/Azure/Azure-DataFactory/tree/master/Samples/DeleteBlobFileFolderCustomActivity).
+> Copy Activity does not delete data from the source after the data is successfully copied to the destination. If you need to delete source data after a successful copy, create a [custom activity](data-factory-use-custom-activities.md) to delete the data and use the activity in the pipeline. For an example, see the [Delete blob or folder sample on GitHub](https://github.com/Azure/Azure-DataFactory/tree/master/SamplesV1/DeleteBlobFileFolderCustomActivity).
 
 ## Get started
 You can create a pipeline with a copy activity that moves data to/from an Azure Blob Storage by using different tools/APIs.

articles/data-factory/v1/data-factory-build-your-first-pipeline-using-vs.md

@@ -538,7 +538,7 @@ To publish entities in an Azure Data Factory project using configuration file:
 When you deploy, the values from the configuration file are used to set values for properties in the JSON files before the entities are deployed to Azure Data Factory service.   
 
 ## Use Azure Key Vault
-It is not advisable and often against security policy to commit sensitive data such as connection strings to the code repository. See [ADF Secure Publish](https://github.com/Azure/Azure-DataFactory/tree/master/Samples/ADFSecurePublish) sample on GitHub to learn about storing sensitive information in Azure Key Vault and using it while publishing Data Factory entities. The Secure Publish extension for Visual Studio allows the secrets to be stored in Key Vault and only references to them are specified in linked services/ deployment configurations. These references are resolved when you publish Data Factory entities to Azure. These files can then be committed to source repository without exposing any secrets.
+It is not advisable and often against security policy to commit sensitive data such as connection strings to the code repository. See [ADF Secure Publish](https://github.com/Azure/Azure-DataFactory/tree/master/SamplesV1/ADFSecurePublish) sample on GitHub to learn about storing sensitive information in Azure Key Vault and using it while publishing Data Factory entities. The Secure Publish extension for Visual Studio allows the secrets to be stored in Key Vault and only references to them are specified in linked services/ deployment configurations. These references are resolved when you publish Data Factory entities to Azure. These files can then be committed to source repository without exposing any secrets.
 
 ## Summary
 In this tutorial, you created an Azure data factory to process data by running Hive script on a HDInsight hadoop cluster. You used the Data Factory Editor in the Azure portal to do the following steps:  

articles/data-factory/v1/data-factory-copy-activity-tutorial-using-visual-studio.md

@@ -506,7 +506,7 @@ To publish entities in an Azure Data Factory project using configuration file:
 When you deploy, the values from the configuration file are used to set values for properties in the JSON files before the entities are deployed to Azure Data Factory service.   
 
 ## Use Azure Key Vault
-It is not advisable and often against security policy to commit sensitive data such as connection strings to the code repository. See [ADF Secure Publish](https://github.com/Azure/Azure-DataFactory/tree/master/Samples/ADFSecurePublish) sample on GitHub to learn about storing sensitive information in Azure Key Vault and using it while publishing Data Factory entities. The Secure Publish extension for Visual Studio allows the secrets to be stored in Key Vault and only references to them are specified in linked services/ deployment configurations. These references are resolved when you publish Data Factory entities to Azure. These files can then be committed to source repository without exposing any secrets.
+It is not advisable and often against security policy to commit sensitive data such as connection strings to the code repository. See [ADF Secure Publish](https://github.com/Azure/Azure-DataFactory/tree/master/SamplesV1/ADFSecurePublish) sample on GitHub to learn about storing sensitive information in Azure Key Vault and using it while publishing Data Factory entities. The Secure Publish extension for Visual Studio allows the secrets to be stored in Key Vault and only references to them are specified in linked services/ deployment configurations. These references are resolved when you publish Data Factory entities to Azure. These files can then be committed to source repository without exposing any secrets.
 
 
 ## Next steps

articles/data-factory/v1/data-factory-data-transformation-activities.md

@@ -68,7 +68,7 @@ Data Lake Analytics U-SQL Activity runs a U-SQL script on an Azure Data Lake Ana
 ## .NET custom activity
 If you need to transform data in a way that is not supported by Data Factory, you can create a custom activity with your own data processing logic and use the activity in the pipeline. You can configure the custom .NET activity to run using either an Azure Batch service or an Azure HDInsight cluster. See [Use custom activities](data-factory-use-custom-activities.md) article for details. 
 
-You can create a custom activity to run R scripts on your HDInsight cluster with R installed. See [Run R Script using Azure Data Factory](https://github.com/Azure/Azure-DataFactory/tree/master/Samples/RunRScriptUsingADFSample). 
+You can create a custom activity to run R scripts on your HDInsight cluster with R installed. See [Run R Script using Azure Data Factory](https://github.com/Azure/Azure-DataFactory/tree/master/SamplesV1/RunRScriptUsingADFSample). 
 
 ## Compute environments
 You create a linked service for the compute environment and then use the linked service when defining a transformation activity. There are two types of compute environments supported by Data Factory. 

articles/data-factory/v1/data-factory-hadoop-streaming-activity.md

@@ -222,5 +222,5 @@ The HDInsight cluster is automatically populated with example programs (wc.exe a
 * [Pig Activity](data-factory-pig-activity.md)
 * [MapReduce Activity](data-factory-map-reduce.md)
 * [Invoke Spark programs](data-factory-spark.md)
-* [Invoke R scripts](https://github.com/Azure/Azure-DataFactory/tree/master/Samples/RunRScriptUsingADFSample)
+* [Invoke R scripts](https://github.com/Azure/Azure-DataFactory/tree/master/SamplesV1/RunRScriptUsingADFSample)
 

articles/data-factory/v1/data-factory-hive-activity.md

@@ -241,5 +241,5 @@ To use parameterized Hive script, do the following
 * [MapReduce Activity](data-factory-map-reduce.md)
 * [Hadoop Streaming Activity](data-factory-hadoop-streaming-activity.md)
 * [Invoke Spark programs](data-factory-spark.md)
-* [Invoke R scripts](https://github.com/Azure/Azure-DataFactory/tree/master/Samples/RunRScriptUsingADFSample)
+* [Invoke R scripts](https://github.com/Azure/Azure-DataFactory/tree/master/SamplesV1/RunRScriptUsingADFSample)
 

articles/data-factory/v1/data-factory-load-sql-data-warehouse.md

@@ -45,7 +45,7 @@ This article provides step-by-step instructions for moving data into Azure SQL D
 
 ## Prerequisites
 * Azure Blob Storage: this experiment uses Azure Blob Storage (GRS) for storing TPC-H testing dataset.  If you do not have an Azure storage account, learn [how to create a storage account](../../storage/common/storage-quickstart-create-account.md).
-* [TPC-H](http://www.tpc.org/tpch/) data: we are going to use TPC-H as the testing dataset.  To do that, you need to use `dbgen` from TPC-H toolkit, which helps you generate the dataset.  You can either download source code for `dbgen` from [TPC Tools](http://www.tpc.org/tpc_documents_current_versions/current_specifications.asp) and compile it yourself, or download the compiled binary from [GitHub](https://github.com/Azure/Azure-DataFactory/tree/master/Samples/TPCHTools).  Run dbgen.exe with the following commands to generate 1 TB flat file for `lineitem` table spread across 10 files:
+* [TPC-H](http://www.tpc.org/tpch/) data: we are going to use TPC-H as the testing dataset.  To do that, you need to use `dbgen` from TPC-H toolkit, which helps you generate the dataset.  You can either download source code for `dbgen` from [TPC Tools](http://www.tpc.org/tpc_documents_current_versions/current_specifications.asp) and compile it yourself, or download the compiled binary from [GitHub](https://github.com/Azure/Azure-DataFactory/tree/master/SamplesV1/TPCHTools).  Run dbgen.exe with the following commands to generate 1 TB flat file for `lineitem` table spread across 10 files:
 
   * `Dbgen -s 1000 -S **1** -C 10 -T L -v`
   * `Dbgen -s 1000 -S **2** -C 10 -T L -v`

articles/data-factory/v1/data-factory-map-reduce.md

@@ -107,7 +107,7 @@ In the JSON definition for the HDInsight Activity:
    You can use the HDInsight MapReduce Activity to run any MapReduce jar file on an HDInsight cluster. In the following sample JSON definition of a pipeline, the HDInsight Activity is configured to run a Mahout JAR file.
 
 ## Sample on GitHub
-You can download a sample for using the HDInsight MapReduce Activity from: [Data Factory Samples on GitHub](https://github.com/Azure/Azure-DataFactory/tree/master/Samples/JSON/MapReduce_Activity_Sample).  
+You can download a sample for using the HDInsight MapReduce Activity from: [Data Factory Samples on GitHub](https://github.com/Azure/Azure-DataFactory/tree/master/SamplesV1/JSON/MapReduce_Activity_Sample).  
 
 ## Running the Word Count program
 The pipeline in this example runs the Word Count Map/Reduce program on your Azure HDInsight cluster.   
@@ -245,5 +245,5 @@ You can use MapReduce activity to run Spark programs on your HDInsight Spark clu
 * [Pig Activity](data-factory-pig-activity.md)
 * [Hadoop Streaming Activity](data-factory-hadoop-streaming-activity.md)
 * [Invoke Spark programs](data-factory-spark.md)
-* [Invoke R scripts](https://github.com/Azure/Azure-DataFactory/tree/master/Samples/RunRScriptUsingADFSample)
+* [Invoke R scripts](https://github.com/Azure/Azure-DataFactory/tree/master/SamplesV1/RunRScriptUsingADFSample)