Merge pull request #260582 from MicrosoftDocs/main

Release pre post scripts--scheduled release at 9:30PM of 12/06

Author: Saisang

.openpublishing.redirection.json

@@ -25737,6 +25737,11 @@
             "redirect_url": "/azure/update-manager/workbooks",
             "redirect_document_id": false
         },
+        {
+            "source_path": "articles/update-manager/whats-upcoming.md",
+            "redirect_url": "/azure/update-manager/whats-new",
+            "redirect_document_id": false
+        },
         {
             "source_path_from_root": "/articles/orbital/delete-contact.md",
 	        "redirect_url": "/azure/orbital/spacecraft-object",

articles/azure-monitor/essentials/data-collection-transformations.md

@@ -24,7 +24,7 @@ The following table describes the different goals that you can achieve by using
 You can apply transformations to the following tables in a Log Analytics workspace:
 
 - Any Azure table listed in [Tables that support transformations in Azure Monitor Logs](../logs/tables-feature-support.md)
-- Any custom table
+- Any custom table created for the Azure Monitor Agent. (MMA custom table can't use transformations)
 
 ## How transformations work
 Transformations are performed in Azure Monitor in the [data ingestion pipeline](../essentials/data-collection.md) after the data source delivers the data and before it's sent to the destination. The data source might perform its own filtering before sending data but then rely on the transformation for further manipulation before it's sent to the destination.

articles/cosmos-db/mongodb/vcore/how-to-private-link.md

@@ -32,8 +32,22 @@ To establish a connection, Azure Cosmos DB for MongoDB vCore with Private Link s
 - An existing Azure Cosmos DB for MongoDB vCore cluster.
   - If you don't have an Azure subscription, [create an account for free](https://azure.microsoft.com/free).
   - If you have an existing Azure subscription, [create a new Azure Cosmos DB for MongoDB vCore cluster](quickstart-portal.md).
-- Access to an active Virtual network and Subnet
+- Access to an active Virtual network and Subnet.
   - If you don’t have a Virtual network, [create a virtual network using the Azure portal](../../../virtual-network/quick-create-portal.md)
+- Verify your access to Azure Cosmos DB for MongoDB vCore Private Endpoint.
+  - If you don’t have access, you can request it by following the steps below.
+
+
+## Requesting Access to Azure Cosmos DB for MongoDB vCore Private Endpoint via Azure Portal
+
+To request access for a private endpoint for an existing Azure Cosmos DB for MongoDB vCore cluster, follow these steps using the Azure portal:
+
+1. Sign in to the [Azure portal](https://portal.azure.com), and search for **Preview Features** in the search bar.
+
+1. Choose **Azure Cosmos DB for MongoDB vCore Private Endpoint** from the available options list and click "register."
+
+1. You will receive a notification once access to the Private Endpoint is granted.
+
 
 ## Create a private endpoint by using the Azure portal
 
@@ -91,6 +105,89 @@ Follow these steps to create a private endpoint for an existing Azure Cosmos DB
 
 When you have an approved Private Endpoint for an Azure Cosmos DB account, in the Azure portal, the **All networks** option in the **Firewall and virtual networks** pane is unavailable.
 
+## Create a private endpoint by using Azure CLI
+
+Run the following Azure CLI script to create a private endpoint named *myPrivateEndpoint* for an existing Azure Cosmos DB account. Replace the variable values with the details for your environment.
+
+```azurecli-interactive
+# Resource group where the Azure Cosmos DB account and virtual network resources are located  
+ResourceGroupName="myResourceGroup" 
+
+# Name of the existing Azure Cosmos DB account  
+MongovCoreClusterName="myMongoCluster" 
+
+# Subscription ID where the Azure Cosmos DB account and virtual network resources are located  
+SubscriptionId="<your Azure subscription ID>"  
+
+# API type of your Azure Cosmos DB account: Sql, SqlDedicated, MongoCluster, Cassandra, Gremlin, or Table 
+CosmosDbSubResourceType="MongoCluster"  
+
+# Name of the virtual network to create  
+VNetName="myVnet"  
+
+# Name of the subnet to create  
+SubnetName="mySubnet"  
+
+# Name of the private endpoint to create  
+PrivateEndpointName="myPrivateEndpoint"  
+
+# Name of the private endpoint connection to create 
+PrivateConnectionName="myConnection" 
+
+az network vnet create \
+  --name $VNetName \
+  --resource-group $ResourceGroupName \
+  --subnet-name $SubnetName 
+
+az network vnet subnet update \
+  --name <name> \
+  --resource-group $ResourceGroupName \
+  --vnet-name $VNetName \
+  --disable-private-endpoint-network-policies true 
+
+az network private-endpoint create \
+  --name $PrivateEndpointName \
+  --resource-group $ResourceGroupName \
+  --vnet-name $VNetName \
+  --subnet $SubnetName \
+  --private-connection-resource-id "/subscriptions/$SubscriptionId/resourceGroups/$ResourceGroupName/providers/Microsoft.DocumentDB/mongoClusters/$MongovCoreClusterName" \
+  --group-ids MongoCluster --connection-name $PrivateConnectionName 
+```
+
+### Integrate the private endpoint with a private DNS zone
+After you create the private endpoint, you can integrate it with a private DNS zone by using the following Azure CLI script:
+
+```azurecli-interactive
+#Zone name differs based on the API type and group ID you are using. 
+zoneName="privatelink.mongocluster.azure.com" 
+
+az network private-dns zone create \
+  --resource-group $ResourceGroupName \
+  --name $zoneName 
+
+az network private-dns link vnet create --resource-group $ResourceGroupName \
+  --zone-name $zoneName \
+  --name <dns-link-name> \
+  --virtual-network $VNetName \
+  --registration-enabled false 
+
+#Create a DNS zone group
+az network private-endpoint dns-zone-group create \
+  --resource-group $ResourceGroupName \
+  --endpoint-name <pe-name> \
+  --name <zone-group-name> \
+  --private-dns-zone $zoneName \
+  --zone-name mongocluster 
+```
+
+## MongoClusters Commands on Private Link 
+```azurecli-interactive
+az network private-link-resource list \
+  -g <rg-name> \
+  -n <resource-name> \
+  --type Microsoft.DocumentDB/mongoClusters 
+```
+
 ## View private endpoints by using the Azure portal
 
 Follow these steps to view a private endpoint for an existing Azure Cosmos DB account by using the Azure portal:

articles/data-manager-for-agri/concepts-ingest-satellite-imagery.md

@@ -7,6 +7,7 @@ ms.service: data-manager-for-agri
 ms.topic: conceptual
 ms.date: 11/17/2023
 ms.custom: template-concept
+show_latex: true
 ---
 
 # Using satellite imagery in Azure Data Manager for Agriculture 
@@ -124,4 +125,4 @@ The image names and resolutions supported by APIs used to ingest and read satell
 
 ## Next steps
 
-* Test our APIs [here](/rest/api/data-manager-for-agri).
+* Test our APIs [here](/rest/api/data-manager-for-agri).

articles/hdinsight/hdinsight-administer-use-portal-linux.md

@@ -4,14 +4,14 @@ description: Learn how to create and manage Azure HDInsight clusters using the A
 ms.service: hdinsight
 ms.topic: conceptual
 ms.custom: hdinsightactive
-ms.date: 11/11/2022
+ms.date: 12/06/2023
 ---
 
 # Manage Apache Hadoop clusters in HDInsight by using the Azure portal
 
 [!INCLUDE [selector](includes/hdinsight-portal-management-selector.md)]
 
-Using the [Azure portal](https://portal.azure.com), you can manage [Apache Hadoop](https://hadoop.apache.org/) clusters in Azure HDInsight. Use the tab selector above for information on managing Hadoop clusters in HDInsight using other tools.
+Using the [Azure portal](https://portal.azure.com), you can manage [Apache Hadoop](https://hadoop.apache.org/) clusters in Azure HDInsight. Use the tab selector for information on managing Hadoop clusters in HDInsight using other tools.
 
 ## Prerequisites
 
@@ -23,13 +23,13 @@ Sign in to [https://portal.azure.com](https://portal.azure.com).
 
 ## <a name="showClusters"></a> List and show clusters
 
-The **HDInsight clusters** page will list your existing clusters.  From the portal:
+The **HDInsight clusters** page lists your existing clusters. From the portal:
 1. Select **All services** from the left menu.
 2. Select **HDInsight clusters** under **ANALYTICS**.
 
 ## <a name="homePage"></a> Cluster home page
 
-Select your cluster name from the [**HDInsight clusters**](#showClusters) page.  This will open the **Overview** view, which looks similar to the following image:
+Select your cluster name from the [**HDInsight clusters**](#showClusters) page.  This opens the **Overview** view, which looks similar to the following image:
 
 :::image type="content" source="./media/hdinsight-administer-use-portal-linux/hdinsight-essentials2.png" alt-text="Azure portal HDInsight cluster essentials":::
 
@@ -98,7 +98,7 @@ From the [cluster home page](#homePage),  under **Settings** select **Properties
 |CLUSTER URL|The URL for the Ambari web interface.|
 |Private Endpoint|The private endpoint for the cluster.|
 |Secure shell (SSH)|The username and host name to use in accessing the cluster via SSH.|
-|STATUS|One of: Aborted, Accepted, ClusterStorageProvisioned, AzureVMConfiguration, HDInsightConfiguration, Operational, Running, Error, Deleting, Deleted, Timedout, DeleteQueued, DeleteTimedout, DeleteError, PatchQueued, CertRolloverQueued, ResizeQueued, or ClusterCustomization.|
+|STATUS|One of: Aborted, Accepted, ClusterStorageProvisioned, AzureVMConfiguration, HDInsightConfiguration, Operational, Running, Error, Deleting, Deleted, Timeout, DeleteQueued, DeleteTimeout, DeleteError, PatchQueued, CertRolloverQueued, ResizeQueued, or ClusterCustomization.|
 |REGION|Azure location. For a list of supported Azure locations, see the **Region** drop-down list box on [HDInsight pricing](https://azure.microsoft.com/pricing/details/hdinsight/).|
 |DATE CREATED|The date the cluster was deployed.|
 |OPERATING SYSTEM|Either **Windows** or **Linux**.|

articles/hdinsight/hdinsight-scaling-best-practices.md

@@ -6,7 +6,7 @@ author: yeturis
 ms.service: hdinsight
 ms.topic: how-to
 ms.custom: seoapr2020
-ms.date: 11/17/2022
+ms.date: 12/07/2023
 ---
 
 # Manually scale Azure HDInsight clusters
@@ -15,7 +15,7 @@ HDInsight provides elasticity with options to scale up and scale down the number
 
 Scale up your cluster before periodic batch processing so the cluster has adequate resources.  After processing completes, and usage goes down, scale down the HDInsight cluster to fewer worker nodes.
 
-You can scale a cluster manually using one of the methods outlined below. You can also use [autoscale](hdinsight-autoscale-clusters.md) options to automatically scale up and down in response to certain metrics.
+You can scale a cluster manually using one of the following methods. You can also use [autoscale](hdinsight-autoscale-clusters.md) options to automatically scale up and down in response to certain metrics.
 
 > [!NOTE]  
 > Only clusters with HDInsight version 3.1.3 or higher are supported. If you are unsure of the version of your cluster, you can check the Properties page.
@@ -42,9 +42,9 @@ Using any of these methods, you can scale your HDInsight cluster up or down with
 
 ## Impact of scaling operations
 
-When you **add** nodes to your running HDInsight cluster (scale up), jobs won't be affected. New jobs can be safely submitted while the scaling process is running. If the scaling operation fails, the failure will leave your cluster in a functional state.
+When you **add** nodes to your running HDInsight cluster (scale up), jobs remains unaffected. New jobs can be safely submitted while the scaling process is running. If the scaling operation fails, the failure  leaves your cluster in a functional state.
 
-If you **remove** nodes (scale down), pending or running jobs will fail when the scaling operation completes. This failure is because of some of the services restarting during the scaling process. Your cluster may get stuck in safe mode during a manual scaling operation.
+If you **remove** nodes (scale down), pending or running jobs fail when the scaling operation completes. This failure is because of some of the services restarting during the scaling process. Your cluster may get stuck in safe mode during a manual scaling operation.
 
 The impact of changing the number of data nodes varies for each type of cluster supported by HDInsight:
 
@@ -72,7 +72,7 @@ The impact of changing the number of data nodes varies for each type of cluster
 
 * Apache Hive LLAP
 
-    After scaling to `N` worker nodes, HDInsight will automatically set the following configurations and restart Hive.
+    After scaling to `N` worker nodes, HDInsight automatically set the following configurations and restart Hive.
 
   * Maximum Total Concurrent Queries: `hive.server2.tez.sessions.per.default.queue = min(N, 32)`
   * Number of nodes used by Hive's LLAP: `num_llap_nodes  = N`
@@ -152,7 +152,7 @@ The following sections describe these options.
 
 Stop all Hive jobs before scaling down to one worker node. If  your workload is scheduled, then execute your scale-down after Hive work is done.
 
-Stopping the Hive jobs before scaling, helps minimize the number of scratch files in the tmp folder (if any).
+Stop the Hive jobs before scaling, helps minimize the number of scratch files in the tmp folder (if any).
 
 #### Manually clean up Hive's scratch files
 
@@ -199,11 +199,11 @@ If Hive has left behind temporary files, then you can manually clean up those fi
 
 If your clusters get stuck in safe mode frequently when scaling down to fewer than three worker nodes, then keep at least three worker nodes.
 
-Having three worker nodes is more costly than scaling down to only one worker node. However, this action will prevent your cluster from getting stuck in safe mode.
+Having three worker nodes is more costly than scaling down to only one worker node. However, this action prevents your cluster from getting stuck in safe mode.
 
 ### Scale HDInsight down to one worker node
 
-Even when the cluster is scaled down to one node, worker node 0 will still survive. Worker node 0 can never be decommissioned.
+Even when the cluster is scaled down to one node, worker node 0 still survive. Worker node 0 can never be decommissioned.
 
 #### Run the command to leave safe mode
 

articles/hdinsight/kafka/apache-kafka-connector-iot-hub.md

@@ -5,7 +5,7 @@ description: Learn how to use Apache Kafka on HDInsight with Azure IoT Hub. The
 ms.service: hdinsight
 ms.topic: how-to
 ms.custom: hdinsightactive
-ms.date: 11/17/2022
+ms.date: 12/05/2023
 ---
 
 # Use Apache Kafka on HDInsight with Azure IoT Hub
@@ -44,11 +44,11 @@ For more information on the Connect API, see [https://kafka.apache.org/documenta
     sbt assembly
     ```
 
-    The build will take a few minutes to complete. The command creates a file named `kafka-connect-iothub-assembly_2.11-0.7.0.jar` in the `toketi-kafka-connect-iothub-master\target\scala-2.11` directory for the project.
+    The build takes a few minutes to complete. The command creates a file named `kafka-connect-iothub-assembly_2.11-0.7.0.jar` in the `toketi-kafka-connect-iothub-master\target\scala-2.11` directory for the project.
 
 ## Install the connector
 
-1. Upload the .jar file to the edge node of your Kafka on HDInsight cluster. Edit the command below by replacing `CLUSTERNAME` with the actual name of your cluster. The default values for the SSH user account and name of [edge node](../hdinsight-apps-use-edge-node.md#access-an-edge-node) are used below, modify as needed.
+1. Upload the .jar file to the edge node of your Kafka on HDInsight cluster. Edit the following command by replacing `CLUSTERNAME` with the actual name of your cluster. The default values for the SSH user account and name of [edge node](../hdinsight-apps-use-edge-node.md#access-an-edge-node) are used and modify as needed.
 
     ```cmd
     scp kafka-connect-iothub-assembly*.jar [email protected]:
@@ -115,7 +115,7 @@ From your SSH connection to the edge node, use the following steps to configure
     |---|---|---|
     |`bootstrap.servers=localhost:9092`|Replace the `localhost:9092` value with the broker hosts from the previous step|Configures the standalone configuration for the edge node to find the Kafka brokers.|
     |`key.converter=org.apache.kafka.connect.json.JsonConverter`|`key.converter=org.apache.kafka.connect.storage.StringConverter`|This change allows you to test using the console producer included with Kafka. You may need different converters for other producers and consumers. For information on using other converter values, see [https://github.com/Azure/toketi-kafka-connect-iothub/blob/master/README_Sink.md](https://github.com/Azure/toketi-kafka-connect-iothub/blob/master/README_Sink.md).|
-    |`value.converter=org.apache.kafka.connect.json.JsonConverter`|`value.converter=org.apache.kafka.connect.storage.StringConverter`|Same as above.|
+    |`value.converter=org.apache.kafka.connect.json.JsonConverter`|`value.converter=org.apache.kafka.connect.storage.StringConverter`|Same as given.|
     |N/A|`consumer.max.poll.records=10`|Add to end of file. This change is to prevent timeouts in the sink connector by limiting it to 10 records at a time. For more information, see [https://github.com/Azure/toketi-kafka-connect-iothub/blob/master/README_Sink.md](https://github.com/Azure/toketi-kafka-connect-iothub/blob/master/README_Sink.md).|
 
 1. To save the file, use __Ctrl + X__, __Y__, and then __Enter__.
@@ -277,7 +277,7 @@ For more information on configuring the connector sink, see [https://github.com/
     > [!NOTE]  
     > You may see several warnings as the connector starts. These warnings do not cause problems with receiving messages from IoT hub.
 
-1. Stop the connector after a few minutes using **Ctrl + C** twice. It will take a few minutes for the connector to stop.
+1. Stop the connector after a few minutes using **Ctrl + C** twice. It takes a few minutes for the connector to stop.
 
 ## Start the sink connector
 
@@ -337,14 +337,15 @@ To send messages through the connector, use the following steps:
 
     The schema for this JSON document is described in more detail at [https://github.com/Azure/toketi-kafka-connect-iothub/blob/master/README_Sink.md](https://github.com/Azure/toketi-kafka-connect-iothub/blob/master/README_Sink.md).
 
-    If you're using the simulated Raspberry Pi device, and it's running, the following message is logged by the device:
+   
+If you're using the simulated Raspberry Pi device, and it's running, the device logs the following message.:
 
-    ```output
-    Receive message: Turn On
-    ```
+```output
+Receive message: Turn On
 
-    Resend the JSON document, but change the value of the `"message"` entry. The new value is logged by the device.
 
+Resend the JSON document, but change the value of the `"message"` entry. The new value is logged by the device.
+ ```
 For more information on using the sink connector, see [https://github.com/Azure/toketi-kafka-connect-iothub/blob/master/README_Sink.md](https://github.com/Azure/toketi-kafka-connect-iothub/blob/master/README_Sink.md).
 
 ## Next steps

articles/payment-hsm/inspect-traffic.md

@@ -1,6 +1,6 @@
 ---
 title: Azure Payment HSM traffic inspection
-description: Guiance on how to bypass the UDR restriction and inspect traffic destined to an Azure Payment HSM.
+description: Guidance on how to bypass the UDR restriction and inspect traffic destined to an Azure Payment HSM.
 services: payment-hsm
 ms.service: payment-hsm
 author: davidsntg