Merge branch 'main' of https://github.com/MicrosoftDocs/azure-docs-pr into net-landing-edit

asudbring · asudbring · commit fc90d884ff1f · 2022-11-21T09:36:47.000-08:00
diff --git a/articles/active-directory/conditional-access/workload-identity.md b/articles/active-directory/conditional-access/workload-identity.md
@@ -6,7 +6,7 @@ services: active-directory
 ms.service: active-directory
 ms.subservice: conditional-access
 ms.topic: how-to
-ms.date: 03/25/2022
+ms.date: 11/21/2022
 
 ms.author: joflore
 author: MicrosoftGuyJFlo
@@ -56,7 +56,7 @@ Create a location based Conditional Access policy that applies to service princi
 
 ### Create a risk-based Conditional Access policy
 
-Create a location based Conditional Access policy that applies to service principals.
+Create a risk-based Conditional Access policy that applies to service principals.
 
 :::image type="content" source="media/workload-identity/conditional-access-workload-identity-risk-policy.png" alt-text="Creating a Conditional Access policy with a workload identity and risk as a condition." lightbox="media/workload-identity/conditional-access-workload-identity-risk-policy.png":::
 
diff --git a/articles/cosmos-db/configure-periodic-backup-restore.md b/articles/cosmos-db/configure-periodic-backup-restore.md
@@ -227,7 +227,6 @@ After you restore the data, you get a notification about the name of the new acc
 
 The following are different ways to migrate data back to the original account:
 
-* Use the [Azure Cosmos DB data migration tool](import-data.md).
 * Use the [Azure Data Factory](../data-factory/connector-azure-cosmos-db.md).
 * Use the [change feed](change-feed.md) in Azure Cosmos DB.
 * You can write your own custom code.
diff --git a/articles/cosmos-db/faq.yml b/articles/cosmos-db/faq.yml
@@ -218,7 +218,6 @@ sections:
           You can bulk-insert documents into Azure Cosmos DB in one of the following ways:
           
           * The bulk executor tool, as described in [Using bulk executor .NET library](nosql/bulk-executor-dotnet.md) and [Using bulk executor Java library](bulk-executor-java.md)
-          * The data migration tool, as described in [Database migration tool for Azure Cosmos DB](import-data.md).
           * Stored procedures, as described in [Server-side JavaScript programming for Azure Cosmos DB](stored-procedures-triggers-udfs.md).
           
       - question: |
diff --git a/articles/cosmos-db/index.yml b/articles/cosmos-db/index.yml
@@ -136,13 +136,11 @@ additionalContent:
       items:
       - title: Migration options
         links:
-          - text: Data migration tool – API for NoSQL
-            url: import-data.md
           - text: CQL shell, Spark – API for Cassandra
             url: cassandra/migrate-data.md
           - text: Azure Database Migration Service – API for MongoDB
             url: ../dms/tutorial-mongodb-cosmos-db.md
-          - text: AzCopy, Data migration tool – API for Table
+          - text: AzCopy – API for Table
             url: table/import.md                         
       - title: Migration guides
         links:
diff --git a/articles/cosmos-db/local-emulator.md b/articles/cosmos-db/local-emulator.md
@@ -28,8 +28,6 @@ The Azure Cosmos DB Emulator provides a high-fidelity emulation of the Azure Cos
 
 While emulation of the Azure Cosmos DB service is faithful, the emulator's implementation is different than the service. For example, the emulator uses standard OS components such as the local file system for persistence, and the HTTPS protocol stack for connectivity. Functionality that relies on the Azure infrastructure like global replication, single-digit millisecond latency for reads/writes, and tunable consistency levels are not applicable when you use the emulator.
 
-You can migrate data between the Azure Cosmos DB Emulator and the Azure Cosmos DB service by using the [Azure Cosmos DB Data Migration Tool](https://github.com/azure/azure-documentdb-datamigrationtool).
-
 ## Differences between the emulator and the cloud service
 
 Because the Azure Cosmos DB Emulator provides an emulated environment that runs on the local developer workstation, there are some differences in functionality between the emulator and an Azure Cosmos DB account in the cloud:
diff --git a/articles/cosmos-db/merge.md b/articles/cosmos-db/merge.md
@@ -169,7 +169,6 @@ To enroll in the preview, your Azure Cosmos DB account must meet all the followi
   - Azure Functions
   - Azure Search
   - Azure Cosmos DB Spark connector
-  - Azure Cosmos DB data migration tool
   - Any third party library or tool that has a dependency on an Azure Cosmos DB SDK that isn't .NET V3 SDK v3.27.0 or higher
 
 ### Account resources and configuration
@@ -209,7 +208,6 @@ If you enroll in the preview, the following connectors will fail.
 - Azure Functions <sup>1</sup>
 - Azure Search <sup>1</sup>
 - Azure Cosmos DB Spark connector <sup>1</sup>
-- Azure Cosmos DB data migration tool
 - Any third party library or tool that has a dependency on an Azure Cosmos DB SDK that isn't .NET V3 SDK v3.27.0 or higher
 
 <sup>1</sup> Support for these connectors is planned for the future.
diff --git a/articles/cosmos-db/migrate.md b/articles/cosmos-db/migrate.md
@@ -20,7 +20,7 @@ Before you migrate the entire workload to Azure Cosmos DB, you can migrate a sub
 
 ## Tools for data migration 
 
-Azure Cosmos DB migration strategies currently differ based on the API choice and the size of the data. To migrate smaller datasets – for validating data modeling, query performance, partition key choice etc. – you can choose the [Data Migration Tool](import-data.md) or [Azure Data Factory’s Azure Cosmos DB connector](../data-factory/connector-azure-cosmos-db.md). If you are familiar with Spark, you can also choose to use the [Azure Cosmos DB Spark connector](./nosql/quickstart-spark.md) to migrate data.
+Azure Cosmos DB migration strategies currently differ based on the API choice and the size of the data. To migrate smaller datasets – for validating data modeling, query performance, partition key choice etc. – you can use [Azure Data Factory’s Azure Cosmos DB connector](../data-factory/connector-azure-cosmos-db.md). If you are familiar with Spark, you can also choose to use the [Azure Cosmos DB Spark connector](./nosql/quickstart-spark.md) to migrate data.
 
 ## Challenges for large-scale migrations 
 
diff --git a/articles/cosmos-db/migration-choices.md b/articles/cosmos-db/migration-choices.md
@@ -45,7 +45,6 @@ If you need help with capacity planning, consider reading our [guide to estimati
 |Migration type|Solution|Supported sources|Supported targets|Considerations|
 |---------|---------|---------|---------|---------|
 |Offline|[Intra-account container copy](intra-account-container-copy.md)|Azure Cosmos DB for NoSQL|Azure Cosmos DB for NoSQL|&bull; CLI-based; No set up needed. <br/>&bull; Supports large datasets.|
-|Offline|[Data Migration Tool](import-data.md)| &bull;JSON/CSV Files<br/>&bull;Azure Cosmos DB for NoSQL<br/>&bull;MongoDB<br/>&bull;SQL Server<br/>&bull;Table Storage<br/>&bull;AWS DynamoDB<br/>&bull;Azure Blob Storage|&bull;Azure Cosmos DB for NoSQL<br/>&bull;Azure Cosmos DB Tables API<br/>&bull;JSON Files |&bull; Easy to set up and supports multiple sources. <br/>&bull; Not suitable for large datasets.|
 |Offline|[Azure Data Factory](../data-factory/connector-azure-cosmos-db.md)| &bull;JSON/CSV Files<br/>&bull;Azure Cosmos DB for NoSQL<br/>&bull;Azure Cosmos DB for MongoDB<br/>&bull;MongoDB <br/>&bull;SQL Server<br/>&bull;Table Storage<br/>&bull;Azure Blob Storage <br/> <br/>See the [Azure Data Factory](../data-factory/connector-overview.md) article for other supported sources.|&bull;Azure Cosmos DB for NoSQL<br/>&bull;Azure Cosmos DB for MongoDB<br/>&bull;JSON Files <br/><br/> See the [Azure Data Factory](../data-factory/connector-overview.md) article for other supported targets. |&bull; Easy to set up and supports multiple sources.<br/>&bull; Makes use of the Azure Cosmos DB bulk executor library. <br/>&bull; Suitable for large datasets. <br/>&bull; Lack of checkpointing - It means that if an issue occurs during the course of migration, you need to restart the whole migration process.<br/>&bull; Lack of a dead letter queue - It means that a few erroneous files can stop the entire migration process.|
 |Offline|[Azure Cosmos DB Spark connector](./nosql/quickstart-spark.md)|Azure Cosmos DB for NoSQL. <br/><br/>You can use other sources with additional connectors from the Spark ecosystem.| Azure Cosmos DB for NoSQL. <br/><br/>You can use other targets with additional connectors from the Spark ecosystem.| &bull; Makes use of the Azure Cosmos DB bulk executor library. <br/>&bull; Suitable for large datasets. <br/>&bull; Needs a custom Spark setup. <br/>&bull; Spark is sensitive to schema inconsistencies and this can be a problem during migration. |
 |Online|[Azure Cosmos DB Spark connector + Change Feed](https://github.com/Azure/azure-sdk-for-java/tree/main/sdk/cosmos/azure-cosmos-spark_3_2-12/Samples/DatabricksLiveContainerMigration)|Azure Cosmos DB for NoSQL. <br/><br/>Uses Azure Cosmos DB Change Feed to stream all historic data as well as live updates.| Azure Cosmos DB for NoSQL. <br/><br/>You can use other targets with additional connectors from the Spark ecosystem.| &bull; Makes use of the Azure Cosmos DB bulk executor library. <br/>&bull; Suitable for large datasets. <br/>&bull; Needs a custom Spark setup. <br/>&bull; Spark is sensitive to schema inconsistencies and this can be a problem during migration. |
@@ -94,10 +93,6 @@ If you need help with capacity planning, consider reading our [guide to estimati
 
 For APIs other than the API for NoSQL, API for MongoDB and the API for Cassandra, there are various tools supported by each of the API's existing ecosystems. 
 
-**API for Table** 
-
-* [Data Migration Tool](table/import.md#data-migration-tool)
-
 **API for Gremlin**
 
 * [Graph bulk executor library](gremlin/bulk-executor-dotnet.md)
diff --git a/articles/cosmos-db/total-cost-ownership.md b/articles/cosmos-db/total-cost-ownership.md
@@ -13,7 +13,7 @@ ms.date: 08/26/2021
 # Total Cost of Ownership (TCO) with Azure Cosmos DB
 [!INCLUDE[NoSQL, MongoDB, Cassandra, Gremlin, Table](includes/appliesto-nosql-mongodb-cassandra-gremlin-table.md)]
 
-Azure Cosmos DB is designed with the fine grained multi-tenancy and resource governance. This design allows Azure Cosmos DB to operate at significantly lower cost and help users save. Currently Azure Cosmos DB supports more than 280 customer workloads on a single machine with the density continuously increasing, and thousands of customer workloads within a cluster. It load balances replicas of customers' workloads across different machines in a cluster and across multiple clusters within a data center. To learn more, see [Azure Cosmos DB: Pushing the frontier of globally distributed databases](https://azure.microsoft.com/blog/azure-cosmos-db-pushing-the-frontier-of-globally-distributed-databases/). Because of  resource-governance, multi-tenancy, and native integration with the rest of Azure infrastructure, Azure Cosmos DB is on average 4 to 6 times cheaper than MongoDB, Cassandra, or other OSS NoSQL running on IaaS and up to 10 times cheaper than the database engines running on premises. See the paper on [The total cost of (non) ownership of a NoSQL database cloud service](https://documentdbportalstorage.blob.core.windows.net/papers/11.15.2017/NoSQL%20TCO%20paper.pdf).
+Azure Cosmos DB is designed with the fine grained multi-tenancy and resource governance. This design allows Azure Cosmos DB to operate at significantly lower cost and help users save. Currently Azure Cosmos DB supports more than 280 customer workloads on a single machine with the density continuously increasing, and thousands of customer workloads within a cluster. It load balances replicas of customers' workloads across different machines in a cluster and across multiple clusters within a data center. To learn more, see [Azure Cosmos DB: Pushing the frontier of globally distributed databases](https://azure.microsoft.com/blog/azure-cosmos-db-pushing-the-frontier-of-globally-distributed-databases/). Because of  resource-governance, multi-tenancy, and native integration with the rest of Azure infrastructure, Azure Cosmos DB is on average 4 to 6 times cheaper than MongoDB, Cassandra, or other OSS NoSQL running on IaaS and up to 10 times cheaper than the database engines running on premises. See the paper on [The total cost of (non) ownership of a NoSQL database cloud service](https://azure.microsoft.com/blog/documentdb-total-cost-of-ownership-for-nosql-databases/).
 
 The OSS NoSQL database solutions, such as Apache Cassandra, MongoDB, HBase, engines were designed for on-premises. When offered as a managed service they are equivalent to a Resource Manager template with a tenant database for managing the provisioned clusters and monitoring support. OSS NoSQL architectures require significant operational overhead, and the expertise can be difficult and expensive to find. On the other hand, Azure Cosmos DB is a fully managed cloud service, which allows developers to focus on business innovation rather than on managing and maintaining database infrastructure.
 
@@ -71,4 +71,4 @@ As an aid for estimating TCO, it can be helpful to start with capacity planning.
 * Learn more about [Optimizing the cost of reads and writes](optimize-cost-reads-writes.md)
 * Learn more about [Optimizing the cost of queries](./optimize-cost-reads-writes.md)
 * Learn more about [Optimizing the cost of multi-region Azure Cosmos DB accounts](optimize-cost-regions.md)
-* Learn more about [The Total Cost of (Non) Ownership of a NoSQL Database Cloud Service](https://documentdbportalstorage.blob.core.windows.net/papers/11.15.2017/NoSQL%20TCO%20paper.pdf)
+* Learn more about [The Total Cost of (Non) Ownership of a NoSQL Database Cloud Service](https://azure.microsoft.com/blog/documentdb-total-cost-of-ownership-for-nosql-databases/)
diff --git a/articles/machine-learning/how-to-monitor-online-endpoints.md b/articles/machine-learning/how-to-monitor-online-endpoints.md
@@ -31,11 +31,24 @@ In this article you learn how to:
 
 ## Metrics
 
-Use the following steps to view metrics for an online endpoint or deployment:
+You can view metrics pages for online endpoints or deployments in the Azure portal. An easy way to access these metrics pages is through links available in the Azure Machine Learning studio user interface—specifically in the **Details** tab of an endpoint's page. Following these links will take you to the exact metrics page in the Azure portal for the endpoint or deployment. Alternatively, you can also go into the Azure portal to search for the metrics page for the endpoint or deployment.
+
+To access the metrics pages through links available in the studio:
+
+1. Go to the [Azure Machine Learning studio](https://ml.azure.com).
+1. In the left navigation bar, select the **Endpoints** page.
+1. Select an endpoint by clicking its name.
+1. Select **View metrics** in the **Attributes** section of the endpoint to open up the endpoint's metrics page in the Azure portal.
+1. Select **View metrics** in the section for each available deployment to open up the deployment's metrics page in the Azure portal.
+
+    :::image type="content" source="media/how-to-monitor-online-endpoints/online-endpoints-access-metrics-from-studio.png" alt-text="A screenshot showing how to access the metrics of an endpoint and deployment from the studio UI." lightbox="media/how-to-monitor-online-endpoints/online-endpoints-access-metrics-from-studio.png":::
+
+To access metrics directly from the Azure portal:
+
 1. Go to the [Azure portal](https://portal.azure.com).
 1. Navigate to the online endpoint or deployment resource.
 
-    online endpoints and deployments are Azure Resource Manager (ARM) resources that can be found by going to their owning resource group. Look for the resource types **Machine Learning online endpoint** and **Machine Learning online deployment**.
+    Online endpoints and deployments are Azure Resource Manager (ARM) resources that can be found by going to their owning resource group. Look for the resource types **Machine Learning online endpoint** and **Machine Learning online deployment**.
 
 1. In the left-hand column, select **Metrics**.
 
diff --git a/articles/machine-learning/media/how-to-monitor-online-endpoints/online-endpoints-access-metrics-from-studio.png b/articles/machine-learning/media/how-to-monitor-online-endpoints/online-endpoints-access-metrics-from-studio.png
