Merge pull request #101202 from nibaccam/datastore

Data| Datastore minor updates

Author: PRMerger6

articles/machine-learning/how-to-access-data.md

@@ -6,10 +6,10 @@ services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
 ms.topic: conceptual
-ms.author: ylxiong
-author: YLXiong1125
+ms.author: sihhu
+author: MayMSFT
 ms.reviewer: nibaccam
-ms.date: 01/13/2020
+ms.date: 01/15/2020
 ms.custom: seodec18
 
 # Customer intent: As an experienced Python developer, I need to make my data in Azure Storage available to my remote compute to train my machine learning models.
@@ -56,13 +56,16 @@ Datastores currently support storing connection information to the storage servi
 Azure&nbsp;SQL&nbsp;Database| SQL authentication <br>Service principal| ✓ | ✓ | ✓ |✓
 Azure&nbsp;PostgreSQL | SQL authentication| ✓ | ✓ | ✓ |✓
 Azure&nbsp;Database&nbsp;for&nbsp;MySQL | SQL authentication|  | ✓ | ✓ |✓
-Databricks&nbsp;File&nbsp;System| No authentication | | ✓`*` | ✓ `*`|✓`*`
+Databricks&nbsp;File&nbsp;System| No authentication | | ✓* | ✓ * |✓* 
 
-`*` only supported on local compute target scenarios
+*only supported on local compute target scenarios
 
 ### Storage guidance
 
-We recommend creating a datastore for an Azure blob container. Both standard and premium storage are available for blobs. Although premium storage is more expensive, its faster throughput speeds might improve the speed of your training runs, particularly if you train against a large dataset. For information about the cost of storage accounts, see the [Azure pricing calculator](https://azure.microsoft.com/pricing/calculator/?service=machine-learning-service).
+We recommend creating a datastore for an Azure blob container.  
+Both standard and premium storage are available for blobs. Although premium storage is more expensive, its faster throughput speeds might improve the speed of your training runs, particularly if you train against a large dataset. For information about the cost of storage accounts, see the [Azure pricing calculator](https://azure.microsoft.com/pricing/calculator/?service=machine-learning-service).
+
+When you create a workspace, an Azure blob container and an Azure file share are automatically registered to the workspace. They're named `workspaceblobstore` and `workspacefilestore`, respectively. They store the connection information for the blob container and the file share that are provisioned in the storage account attached to the workspace. The `workspaceblobstore` container is set as the default datastore.
 
 <a name="access"></a>
 
@@ -121,15 +124,15 @@ account_name=os.getenv("FILE_SHARE_ACCOUNTNAME", "<my-account-name>") # Storage
 account_key=os.getenv("FILE_SHARE_ACCOUNT_KEY", "<my-account-key>") # Storage account key
 
 file_datastore = Datastore.register_azure_file_share(workspace=ws,
-                                                 datastore_name=file_datastore_name, 
-                                                 file_share_name=file_share_name, 
-                                                 account_name=account_name,
-                                                 account_key=account_key)
+                                                     datastore_name=file_datastore_name, 
+                                                     file_share_name=file_share_name, 
+                                                     account_name=account_name,
+                                                     account_key=account_key)
 ```
 
 #### Azure Data Lake Storage Generation 2
 
-For an Azure Data Lake Storage Generation 2 (ADLS Gen 2) datastore, use [register_azure_data_lake_gen2()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.datastore.datastore?view=azure-ml-py#register-azure-data-lake-gen2-workspace--datastore-name--filesystem--account-name--tenant-id--client-id--client-secret--resource-url-none--authority-url-none--protocol-none--endpoint-none--overwrite-false-) to register a credential datastore connected to an Azure DataLake Gen 2 storage with service principal permissions. Learn more about [access control et up for ADLS Gen 2](https://docs.microsoft.com/azure/storage/blobs/data-lake-storage-access-control). 
+For an Azure Data Lake Storage Generation 2 (ADLS Gen 2) datastore, use [register_azure_data_lake_gen2()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.datastore.datastore?view=azure-ml-py#register-azure-data-lake-gen2-workspace--datastore-name--filesystem--account-name--tenant-id--client-id--client-secret--resource-url-none--authority-url-none--protocol-none--endpoint-none--overwrite-false-) to register a credential datastore connected to an Azure DataLake Gen 2 storage with service principal permissions. Learn more about [access control set up for ADLS Gen 2](https://docs.microsoft.com/azure/storage/blobs/data-lake-storage-access-control). 
 
 The following code creates and registers the `adlsgen2_datastore_name` datastore to the `ws` workspace. This datastore accesses the file system `test` on the `account_name` storage account, by using the provided service principal credentials.
 
@@ -144,14 +147,13 @@ tenant_id=os.getenv("ADLSGEN2_TENANT", "<my_tenant_id>") # tenant id of service
 client_id=os.getenv("ADLSGEN2_CLIENTID", "<my_client_id>") # client id of service principal
 client_secret=os.getenv("ADLSGEN2_CLIENT_SECRET", "<my_client_secret>") # the secret of service principal
 
-adlsgen2_datastore = Datastore.register_azure_data_lake_gen2(
-    workspace=ws,
-    datastore_name=adlsgen2_datastore_name,
-    account_name=account_name, # ADLS Gen2 account name
-    filesystem='test', # Name of ADLS Gen2 filesystem
-    tenant_id=tenant_id, # tenant id of service principal
-    client_id=client_id, # client id of service principal
-    client_secret=client_secret) # the secret of service principal
+adlsgen2_datastore = Datastore.register_azure_data_lake_gen2(workspace=ws,
+                                                             datastore_name=adlsgen2_datastore_name,
+                                                             account_name=account_name, # ADLS Gen2 account name
+                                                             filesystem='test', # ADLS Gen2 filesystem
+                                                             tenant_id=tenant_id, # tenant id of service principal
+                                                             client_id=client_id, # client id of service principal
+                                                             client_secret=client_secret) # the secret of service principal
 ```
 
 ### Azure Machine Learning studio 
@@ -189,8 +191,6 @@ for name, datastore in datastores.items():
     print(name, datastore.datastore_type)
 ```
 
-When you create a workspace, an Azure blob container and an Azure file share are automatically registered to the workspace. They're named `workspaceblobstore` and `workspacefilestore`, respectively. They store the connection information for the blob container and the file share that are provisioned in the storage account attached to the workspace. The `workspaceblobstore` container is set as the default datastore.
-
 To get the workspace's default datastore, use this line:
 
 ```Python