Merge pull request #176796 from v-lanjli/removepreview

remove preview

Author: PRMerger8

articles/synapse-analytics/spark/apache-spark-azure-log-analytics.md

@@ -1,5 +1,5 @@
 ---
-title: Monitor Apache Spark applications with Azure Log Analytics (preview)
+title: Monitor Apache Spark applications with Azure Log Analytics
 description: Learn how to enable the Synapse Studio connector for collecting and sending the Apache Spark application metrics and logs to your Log Analytics workspace.
 services: synapse-analytics 
 author: jejiang
@@ -11,7 +11,7 @@ ms.subservice: spark
 ms.date: 03/25/2021
 ms.custom: references_regions
 ---
-# Monitor Apache Spark applications with Azure Log Analytics (preview)
+# Monitor Apache Spark applications with Azure Log Analytics
 
 In this tutorial, you learn how to enable the Synapse Studio connector that's built in to Log Analytics. You can then collect and send Apache Spark application metrics and logs to your [Log Analytics workspace](../../azure-monitor/logs/quick-create-workspace.md). Finally, you can use an Azure Monitor workbook to visualize the metrics and logs.
 
@@ -26,7 +26,7 @@ Consult one of the following resources to create this workspace:
 - [Create a workspace with Azure CLI](../../azure-monitor/logs/resource-manager-workspace.md)
 - [Create and configure a workspace in Azure Monitor by using PowerShell](../../azure-monitor/logs/powershell-workspace-configuration.md)
 
-### Step 2: Prepare a Apache Spark configuration file
+### Step 2: Prepare an Apache Spark configuration file
 
 Use any of the following options to prepare the file.
 
@@ -105,7 +105,7 @@ spark.synapse.logAnalytics.keyVault.linkedServiceName <LINKED_SERVICE_NAME>
 
 | Configuration name | Default value | Description |
 | ------------------ | ------------- | ----------- |
-| spark.synapse.logAnalytics.enabled | false | To enable the Log Analytics sink for the Spark applications, true. Otherwise, false. |
+| spark.synapse.logAnalytics.enabled | False | To enable the Log Analytics sink for the Spark applications, true. Otherwise, false. |
 | spark.synapse.logAnalytics.workspaceId | - | The destination Log Analytics workspace ID. |
 | spark.synapse.logAnalytics.secret | - | The destination Log Analytics workspace secret. |
 | spark.synapse.logAnalytics.keyVault.linkedServiceName   | - | The Key Vault linked service name for the Log Analytics workspace ID and key. |
@@ -124,7 +124,7 @@ spark.synapse.logAnalytics.keyVault.linkedServiceName <LINKED_SERVICE_NAME>
 [uri_suffix]: ../../azure-monitor/logs/data-collector-api.md#request-uri
 
 
-### Step 3: Upload your Apache Spark configuration to a Apache Spark pool
+### Step 3: Upload your Apache Spark configuration to an Apache Spark pool
 You can upload the configuration file to your Azure Synapse Analytics Apache Spark pool. In Synapse Studio:
 
    1. Select **Manage** > **Apache Spark pools**.
@@ -140,13 +140,13 @@ You can upload the configuration file to your Azure Synapse Analytics Apache Spa
 >
 > All the Apache Spark applications submitted to the Apache Spark pool will use the configuration setting to push the Apache Spark application metrics and logs to your specified workspace.
 
-## Submit a Apache Spark application and view the logs and metrics
+## Submit an Apache Spark application and view the logs and metrics
 
 Here's how:
 
-1. Submit a Apache Spark application to the Apache Spark pool configured in the previous step. You can use any of the following ways to do so:
+1. Submit an Apache Spark application to the Apache Spark pool configured in the previous step. You can use any of the following ways to do so:
     - Run a notebook in Synapse Studio. 
-    - In Synapse Studio, submit an Apache Spark batch job through a Apache Spark job definition.
+    - In Synapse Studio, submit an Apache Spark batch job through an Apache Spark job definition.
     - Run a pipeline that contains Apache Spark activity.
 
 1. Go to the specified Log Analytics workspace, and then view the application metrics and logs when the Apache Spark application starts to run.
@@ -240,20 +240,20 @@ Users can query to evaluate metrics and logs at a set frequency, and fire an ale
 
 After the Synapse workspace is created with [data exfiltration protection](../security/workspace-data-exfiltration-protection.md) enabled.
 
-when you want to enabled this feature, you need to create managed private endpoint connection requests to [Azure Monitor private link scopes (AMPLS)](../../azure-monitor/logs/private-link-security.md) in the workspace’s approved Azure AD tenants.
+When you want to enabled this feature, you need to create managed private endpoint connection requests to [Azure Monitor private link scopes (AMPLS)](../../azure-monitor/logs/private-link-security.md) in the workspace’s approved Azure AD tenants.
 
 You can follow below steps to create a managed private endpoint connection to Azure Monitor private link scopes (AMPLS):
 
-1. If there is no existing AMPLS, please follow [Azure Monitor Private Link connection setup](../../azure-monitor/logs/private-link-security.md) to create one.
+1. If there is no existing AMPLS, you can follow [Azure Monitor Private Link connection setup](../../azure-monitor/logs/private-link-security.md) to create one.
 2. Navigate to your AMPLS in Azure portal, on the **Azure Monitor Resources** page, click **Add** to add connection to your Azure Log Analytics workspace.
-3. Navigate to **Synapse Studio > Manage > Managed private endpoints**, click **New** button, select **Azure Monitor Private Link Scopes** and **continue**.
+3. Navigate to **Synapse Studio > Manage > Managed private endpoints**, click **New** button, select **Azure Monitor Private Link Scopes**, and **continue**.
    > [!div class="mx-imgBorder"]
    > ![Create AMPLS managed private endpoint 1](./media/apache-spark-azure-log-analytics/create-ampls-private-endpoint-1.png)
-4. Choose your Azure Monitor Private Link Scope you just created, and click **Create** button.
+4. Choose your Azure Monitor Private Link Scope you created, and click **Create** button.
    > [!div class="mx-imgBorder"]
    > ![Create AMPLS managed private endpoint 2](./media/apache-spark-azure-log-analytics/create-ampls-private-endpoint-2.png)
 5. Wait a few minutes for private endpoint provisioning.
-6. Navigate to your AMPLS in Azure portal again, on the **Private Endpoint connections** page, select the connection just provisioned and **Approve**.
+6. Navigate to your AMPLS in Azure portal again, on the **Private Endpoint connections** page, select the connection provisioned and **Approve**.
 
 > [!NOTE] 
 >  - The AMPLS object has a number of limits you should consider when planning your Private Link setup. See [AMPLS limits](../../azure-monitor/logs/private-link-security.md) for a deeper review of these limits. 

articles/synapse-analytics/spark/azure-synapse-diagnostic-emitters-azure-eventhub.md

@@ -1,5 +1,5 @@
 ---
-title: Collect your Apache Spark applications logs and metrics using Azure Event Hubs (preview)
+title: Collect your Apache Spark applications logs and metrics using Azure Event Hubs 
 description: In this tutorial, you learn how to use the Synapse Apache Spark diagnostic emitter extension to emit Apache Spark applications’ logs, event logs and metrics to your Azure Event Hubs.
 services: synapse-analytics 
 author: hrasheed-msft
@@ -11,11 +11,11 @@ ms.subservice: spark
 ms.date: 08/31/2021
 ---
 
-# Collect your Apache Spark applications logs and metrics using Azure Event Hubs (preview)
+# Collect your Apache Spark applications logs and metrics using Azure Event Hubs 
 
 The Synapse Apache Spark diagnostic emitter extension is a library that enables the Apache Spark application to emit the logs, event logs, and metrics to one or more destinations, including Azure Log Analytics, Azure Storage, and Azure Event Hubs. 
 
-In this tutorial, you learn how to use the Synapse Apache Spark diagnostic emitter extension to emit Apache Spark applications’ logs, event logs and metrics to your Azure Event Hubs.
+In this tutorial, you learn how to use the Synapse Apache Spark diagnostic emitter extension to emit Apache Spark applications’ logs, event logs, and metrics to your Azure Event Hubs.
 
 ## Collect logs and metrics to Azure Event Hubs
 
@@ -24,7 +24,7 @@ In this tutorial, you learn how to use the Synapse Apache Spark diagnostic emitt
 To collect diagnostic logs and metrics to Azure Event Hubs, you can use existing Azure Event Hubs instance.
 Or if you don't have one, you can [create an event hub](../../event-hubs/event-hubs-create.md).
 
-### Step 2: Create a Apache Spark configuration file
+### Step 2: Create an Apache Spark configuration file
 
 Create a `diagnostic-emitter-azure-event-hub-conf.txt` and copy following contents to the file. Or download a [sample template file](https://go.microsoft.com/fwlink/?linkid=2169375) for Apache Spark pool configuration.
 
@@ -36,7 +36,7 @@ spark.synapse.diagnostic.emitter.MyDestination1.secret <connection-string>
 ```
 
 Fill in the following parameters in the configuration file: `<connection-string>`.
-For more description of the parameters, please refer to [Azure EventHub configurations](#available-configurations)
+For more description of the parameters, you can refer to [Azure EventHub configurations](#available-configurations)
 
 ### Step 3: Upload the Apache Spark configuration file to Apache Spark pool
 
@@ -91,7 +91,7 @@ Here is a sample log record in JSON format:
 
 ## Synapse workspace with data exfiltration protection enabled
 
-Azure Synapse Analytics workspaces support enabling data exfiltration protection for workspaces. With exfiltration protection, the logs and metrics can not be sent out to the destination endpoints directly. You can create corresponding [managed private endpoints](../../synapse-analytics/security/synapse-workspace-managed-private-endpoints.md) for different destination endpoints or [create IP firewall rules](../../synapse-analytics/security/synapse-workspace-ip-firewall.md) in this scenario.
+Azure Synapse Analytics workspaces support enabling data exfiltration protection for workspaces. With exfiltration protection, the logs and metrics cannot be sent out to the destination endpoints directly. You can create corresponding [managed private endpoints](../../synapse-analytics/security/synapse-workspace-managed-private-endpoints.md) for different destination endpoints or [create IP firewall rules](../../synapse-analytics/security/synapse-workspace-ip-firewall.md) in this scenario.
 
 
 

articles/synapse-analytics/spark/azure-synapse-diagnostic-emitters-azure-storage.md

@@ -1,5 +1,5 @@
 ---
-title: Collect your Apache Spark applications logs and metrics using Azure Storage account(preview)
+title: Collect your Apache Spark applications logs and metrics using Azure Storage account
 description: This article shows how to use the Synapse Spark diagnostic emitter extension to collect logs, event logs and metrics.cluster and learn how to integrate the Grafana dashboards.
 services: synapse-analytics 
 author: hrasheed-msft
@@ -11,11 +11,11 @@ ms.subservice: spark
 ms.date: 08/31/2021
 ---
 
-# Collect your Apache Spark applications logs and metrics using Azure Storage account(preview)
+# Collect your Apache Spark applications logs and metrics using Azure Storage account
 
 The Synapse Apache Spark diagnostic emitter extension is a library that enables the Apache Spark application to emit the logs, event logs, and metrics to one or more destinations, including Azure Log Analytics, Azure Storage, and Azure Event Hubs. 
 
-In this tutorial, you learn how to use the Synapse Apache Spark diagnostic emitter extension to emit Apache Spark applications’ logs, event logs and metrics to your Azure storage account.
+In this tutorial, you learn how to use the Synapse Apache Spark diagnostic emitter extension to emit Apache Spark applications’ logs, event logs, and metrics to your Azure storage account.
 
 ## Collect logs and metrics to storage account
 
@@ -37,7 +37,7 @@ spark.synapse.diagnostic.emitter.MyDestination1.secret <storage-access-key>
 ```
 
 Fill in the following parameters in the configuration file: `<my-blob-storage>`, `<container-name>`, `<folder-name>`, `<storage-access-key>`.
-For more description of the parameters, please refer to [Azure Storage configurations](#available-configurations)
+For more description of the parameters, you can refer to [Azure Storage configurations](#available-configurations)
 
 ### Step 3: Upload the Apache Spark configuration file to Spark pool
 
@@ -50,7 +50,7 @@ For more description of the parameters, please refer to [Azure Storage configura
 
 After you submit a job to the configured Apache Spark pool, you should be able to see the logs and metrics files in destination storage account.
 The logs will be placed in corresponding paths according to different applications by `<workspaceName>.<sparkPoolName>.<livySessionId>`.
-All the logs files will be in JSON lines format (also called newline-delimited JSON, ndjson), which is very convenient for data processing.
+All the logs files will be in JSON lines format (also called newline-delimited JSON, ndjson), which is convenient for data processing.
 
 ## Available configurations
 
@@ -106,6 +106,6 @@ Azure Synapse Analytics workspaces support enabling data exfiltration protection
    > [!div class="mx-imgBorder"]
    > ![Create managed private endpoint 2](./media/azure-synapse-diagnostic-emitters-azure-storage/create-private-endpoint-2.png)
 3. Wait a few minutes for private endpoint provisioning.
-4. Navigate to your storage account in Azure portal, on the **Networking** > **Private Endpoint connections** page, select the connection just provisioned and **Approve**.
+4. Navigate to your storage account in Azure portal, on the **Networking** > **Private Endpoint connections** page, select the connection provisioned and **Approve**.