Merge pull request #31758 from MicrosoftDocs/main

9/04/2024 AM Publish

Author: Albertyang0

docs/big-data-cluster/big-data-cluster-overview.md

@@ -97,7 +97,7 @@ A SQL Server big data cluster includes a scalable HDFS *storage pool*. This can
 
 Management and monitoring are provided through a combination of command-line tools, APIs, portals, and dynamic management views.
 
-You can use [Azure Data Studio](../azure-data-studio/what-is-azure-data-studio.md) to perform a variety of tasks on the big data cluster:
+You can use [Azure Data Studio](/azure-data-studio/what-is-azure-data-studio) to perform a variety of tasks on the big data cluster:
 
 - Built-in snippets for common management tasks.
 - Ability to browse HDFS, upload files, preview files, and create directories.

docs/big-data-cluster/cluster-monitor-ads.md

@@ -19,7 +19,7 @@ This article explains how to view the status of a big data cluster using Azure D
 
 ## <a id="datastudio"></a> Use Azure Data Studio
 
-After downloading the latest **insiders build** of [Azure Data Studio](../azure-data-studio/download-azure-data-studio.md), you can view service endpoints and the status of a big data cluster with the SQL Server big data cluster dashboard. Some of the features below are only first available in the insiders build of Azure Data Studio.
+After downloading the latest **insiders build** of [Azure Data Studio](/azure-data-studio/download-azure-data-studio), you can view service endpoints and the status of a big data cluster with the SQL Server big data cluster dashboard. Some of the features below are only first available in the insiders build of Azure Data Studio.
 
 1. First, create a connection to your big data cluster in Azure Data Studio. For more information, see [Connect to a SQL Server big data cluster with Azure Data Studio](connect-to-big-data-cluster.md).
 

docs/big-data-cluster/data-virtualization-csv.md

@@ -20,7 +20,7 @@ SQL Server Big Data Clusters can virtualize data from CSV files in HDFS. This pr
 ## Prerequisites
 
 - [A deployed big data cluster](deployment-guidance.md)
-- [Azure Data Studio](../azure-data-studio/download-azure-data-studio.md)
+- [Azure Data Studio](/azure-data-studio/download-azure-data-studio)
 
 ## Select or upload a CSV file for data virtualization
 

docs/big-data-cluster/deploy-big-data-tools.md

@@ -28,8 +28,8 @@ The following table lists common big data cluster tools and how to install them:
 | `python` | Yes | Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Many parts of big data clusters for SQL Server use python. | [Install python](#python)|
 | [!INCLUDE [azure-data-cli-azdata](../includes/azure-data-cli-azdata.md)] | Yes | Command-line tool for installing and managing a big data cluster. | [Install](../azdata/install/deploy-install-azdata.md) |
 | `kubectl`<sup>1</sup> | Yes | Command-line tool for monitoring the underlying Kubernetes cluster ([More info](https://kubernetes.io/docs/tasks/tools/install-kubectl/)). | [Windows](https://kubernetes.io/docs/tasks/tools/install-kubectl/#install-with-powershell-from-psgallery) \| [Linux](https://kubernetes.io/docs/tasks/tools/install-kubectl/#install-using-native-package-management) |
-| **Azure Data Studio** | Yes | Cross-platform graphical tool for querying SQL Server. | [Install](../azure-data-studio/download-azure-data-studio.md) |
-| **Data Virtualization extension** | Yes | Extension for Azure Data Studio that provides a Data Virtualization wizard. | [Install](../azure-data-studio/extensions/data-virtualization-extension.md) |
+| **Azure Data Studio** | Yes | Cross-platform graphical tool for querying SQL Server. | [Install](/azure-data-studio/download-azure-data-studio) |
+| **Data Virtualization extension** | Yes | Extension for Azure Data Studio that provides a Data Virtualization wizard. | [Install](/azure-data-studio/extensions/data-virtualization-extension) |
 | **Azure CLI**<sup>2</sup> | For AKS | Modern command-line interface for managing Azure services. Used with AKS big data cluster deployments ([More info](/cli/azure/)). | [Install](/cli/azure/install-azure-cli) |
 | **mssql-cli** | Optional | Modern command-line interface for querying SQL Server ([More info](../tools/mssql-cli.md)). | [Windows](https://github.com/dbcli/mssql-cli/blob/master/doc/installation/windows.md) \| [Linux](https://github.com/dbcli/mssql-cli/blob/master/doc/installation/linux.md) |
 | **sqlcmd** | For some scripts | Legacy command-line tool for querying SQL Server ([More info](../tools/sqlcmd/sqlcmd-utility.md)). You might need to install the Microsoft ODBC Driver 11 for SQL Server before installing the SQLCMD package. | [Windows](https://www.microsoft.com/download/details.aspx?id=36433) \| [Linux](../linux/sql-server-linux-setup-tools.md) |
@@ -82,7 +82,7 @@ The remaining tools are only required in certain scenarios. **Azure CLI** can be
 
 Azure Data Studio provides capabilities and features specifically for SQL Server Big Data Clusters.
 
-[Get the latest Azure Data Studio](../azure-data-studio/download-azure-data-studio.md).
+[Get the latest Azure Data Studio](/azure-data-studio/download-azure-data-studio).
 
 For details about the latest release, see the [release notes](./release-notes-big-data-cluster.md).
 

docs/big-data-cluster/deployment-guidance.md

@@ -79,7 +79,7 @@ Before deploying a SQL Server 2019 big data cluster, first [install the big data
 - [!INCLUDE [azure-data-cli-azdata](../includes/azure-data-cli-azdata.md)]
 - `kubectl`
 - Azure Data Studio
-- [Data Virtualization extension](../azure-data-studio/extensions/data-virtualization-extension.md) for Azure Data Studio
+- [Data Virtualization extension](/azure-data-studio/extensions/data-virtualization-extension) for Azure Data Studio
 - [Azure CLI](/cli/azure/install-azure-cli), if deploying to AKS
 
 

docs/big-data-cluster/known-issues.md

@@ -144,7 +144,7 @@ ms.topic: troubleshooting-known-issue
 
 - **Issue and customer effect**: For new big data clusters deployed using SQL Server Big Data Clusters CU5, gateway username isn't `root`. If the application used to connect to gateway endpoint is using the wrong credentials, you'll see an authentication error. This change is a result of running applications within the big data cluster as non-root user (a new default behavior starting with SQL Server Big Data Clusters CU5 release, when you deploy a new big data cluster using CU5, the username for the gateway endpoint is based on the value passed through `AZDATA_USERNAME` environment variable. It is the same username used for the controller and SQL Server endpoints. This only affects new deployments. Existing big data clusters deployed with any of the previous releases continue to use `root`. There is no effect to credentials when the cluster is deployed to use Active Directory authentication.
 
-- **Workaround**: Azure Data Studio handles the credentials change transparently for the connection made to gateway to enable HDFS browsing experience in the Object Explorer. You must install [latest Azure Data Studio release](../azure-data-studio/download-azure-data-studio.md) that includes the necessary changes that address this use case.
+- **Workaround**: Azure Data Studio handles the credentials change transparently for the connection made to gateway to enable HDFS browsing experience in the Object Explorer. You must install [latest Azure Data Studio release](/azure-data-studio/download-azure-data-studio) that includes the necessary changes that address this use case.
 For other scenarios where  you must provide credentials for accessing service through the gateway (for example, logging in with [!INCLUDE [azure-data-cli-azdata](../includes/azure-data-cli-azdata.md)], accessing web dashboards for Spark), you must ensure the correct credentials are used. If you're targeting an existing cluster deployed before CU5 you'll continue using the `root` username to connect to gateway, even after upgrading the cluster to CU5. If you deploy a new cluster using CU5 build, sign in by providing the username corresponding to `AZDATA_USERNAME` environment variable.
 
 ### Pods and nodes metrics not being collected

docs/big-data-cluster/manage-with-controller-dashboard.md

@@ -24,8 +24,8 @@ In addition to **azdata** and the cluster status notebook, there is another way
 
 The following prerequisites are required to launch the notebook:
 
-* Latest version of [Azure Data Studio](../azure-data-studio/download-azure-data-studio.md)
-* [[!INCLUDE[sql-server-2019](../includes/sssql19-md.md)] extension installed in Azure Data Studio](../azure-data-studio/data-virtualization-extension.md)
+* Latest version of [Azure Data Studio](/azure-data-studio/download-azure-data-studio)
+* [[!INCLUDE[sql-server-2019](../includes/sssql19-md.md)] extension installed in Azure Data Studio](/azure-data-studio/extensions/data-virtualization-extension)
 
 In addition to above, SQL Server 2019 Big Data Cluster also requires:
 

docs/big-data-cluster/non-root-containers.md

@@ -44,7 +44,7 @@ As a result of services within BDC running as non-root users, credentials used f
 
 ## Use the latest Azure Data Studio
 
-Azure Data Studio handles the credentials change transparently for the connection made through gateway to enable HDFS browsing experience in the Object Explorer or submitting Spark jobs through notebooks. Install the [latest Azure Data Studio insiders build](../azure-data-studio/download-azure-data-studio.md#download-the-insiders-build-of-azure-data-studio). This build includes the necessary changes for this use case.
+Azure Data Studio handles the credentials change transparently for the connection made through gateway to enable HDFS browsing experience in the Object Explorer or submitting Spark jobs through notebooks. Install the [latest Azure Data Studio insiders build](/azure-data-studio/download-azure-data-studio#download-the-insiders-build-of-azure-data-studio). This build includes the necessary changes for this use case.
 
 For other scenarios where you must provide credentials for accessing the service through the gateway (e.g. logging in with [!INCLUDE [azure-data-cli-azdata](../includes/azure-data-cli-azdata.md)], accessing web dashboards for Spark), ensure the correct credentials are used. If you are targeting an existing cluster deployed before CU5 you will continue using `root` username to connect to gateway, even after upgrading the cluster to CU5. If you deploy a new cluster using CU5 build, you will login by providing the username corresponding to `AZDATA_USERNAME` environment variable.
 

docs/big-data-cluster/notebooks-deploy.md

@@ -20,7 +20,7 @@ ms.custom: intro-deployment
 
 SQL Server provides an extension for Azure Data Studio that includes deployment notebooks. A deployment notebook includes documentation and code that you can use in Azure Data Studio to create a SQL Server big data cluster.
 
-Implemented initially as an open-source project, [notebooks](../azure-data-studio/notebooks/notebooks-guidance.md) have been implemented into [Azure Data Studio](../azure-data-studio/download-azure-data-studio.md). You can use markdown for text in the text cells and one of the available kernels to write code in the code cells.
+Implemented initially as an open-source project, [notebooks](/azure-data-studio/notebooks/notebooks-guidance) have been implemented into [Azure Data Studio](/azure-data-studio/download-azure-data-studio). You can use markdown for text in the text cells and one of the available kernels to write code in the code cells.
 
 You can use notebooks to deploy [!INCLUDE[big-data-clusters-nover](../includes/ssbigdataclusters-ss-nover.md)].
 

docs/big-data-cluster/notebooks-manage-bdc.md

@@ -19,7 +19,7 @@ ms.topic: how-to
 
 [!INCLUDE[sql-server-2019](../includes/sssql19-md.md)] provides an extension for Azure Data Studio that includes notebooks. A notebook provides documentation and code that you can use in Azure Data Studio to manage SQL Server 2019 Big Data Clusters.
 
-Originally implemented as an open-source project, [notebooks](../azure-data-studio/notebooks/notebooks-guidance.md) have been incorporated into [Azure Data Studio](../azure-data-studio/download-azure-data-studio.md). You can use markdown for text in the text cells and one of the available kernels to write code in the code cells.
+Originally implemented as an open-source project, [notebooks](/azure-data-studio/notebooks/notebooks-guidance) have been incorporated into [Azure Data Studio](/azure-data-studio/download-azure-data-studio). You can use markdown for text in the text cells and one of the available kernels to write code in the code cells.
 
 You can use notebooks to deploy big data clusters for [!INCLUDE[sql-server-2019](../includes/sssql19-md.md)].
 
@@ -29,7 +29,7 @@ In addition to notebooks, you can view a collection of notebooks, which is calle
 
 You need these prerequisites to open a notebook:
 
-* The latest version of [Azure Data Studio](../azure-data-studio/download-azure-data-studio.md)
+* The latest version of [Azure Data Studio](/azure-data-studio/download-azure-data-studio)
 * The [!INCLUDE[sql-server-2019](../includes/sssql19-md.md)] extension, installed in Azure Data Studio
 
 In addition to those prerequisites, to deploy SQL Server 2019 Big Data Clusters, you also need:
@@ -93,6 +93,6 @@ To change the SQL Server big data cluster for a notebook:
 
 ## Next steps
 
-For more information about notebooks in Azure Data Studio, see [How to use notebooks with SQL Server](../azure-data-studio/notebooks/notebooks-guidance.md).
+For more information about notebooks in Azure Data Studio, see [How to use notebooks with SQL Server](/azure-data-studio/notebooks/notebooks-guidance).
 
 For the location of big data cluster administration notebooks, see [Where to find SQL Server Big Data Clusters administration notebooks](view-cluster-status.md#where-to-find--administration-notebooks).

docs/big-data-cluster/notebooks-tutorial-spark.md

@@ -69,11 +69,11 @@ You can run each notebook cell by pressing the play button to the left of the ce
 
 Run each of the cells in the sample notebook in succession. For more information about using notebooks with [!INCLUDE[big-data-clusters-2019](../includes/ssbigdataclusters-ss-nover.md)], see the following resources:
 
-- [How to use notebooks](../azure-data-studio/notebooks/notebooks-guidance.md)
+- [How to use notebooks](/azure-data-studio/notebooks/notebooks-guidance)
 - [How to manage notebooks in Azure Data Studio](notebooks-manage-bdc.md)
 
 ## Next steps
 
 Learn more about notebooks:
 > [!div class="nextstepaction"]
-> [How to use notebooks](../azure-data-studio/notebooks/notebooks-guidance.md)
+> [How to use notebooks](/azure-data-studio/notebooks/notebooks-guidance)

docs/big-data-cluster/quickstart-big-data-cluster-deploy-aro.md

@@ -35,7 +35,7 @@ The default big data cluster deployment used here consists of a SQL Master insta
 - [Python minimum version 3.0](https://www.python.org/downloads)
 - [`az` CLI](/cli/azure/install-azure-cli/)
 - [[!INCLUDE [azure-data-cli-azdata](../includes/azure-data-cli-azdata.md)]](../azdata/install/deploy-install-azdata.md)
-- [Azure Data Studio](../azure-data-studio/download-azure-data-studio.md)
+- [Azure Data Studio](/azure-data-studio/download-azure-data-studio)
 
 ## Log in to your Azure account
 

docs/big-data-cluster/release-notes-cumulative-updates-history.md

@@ -312,7 +312,7 @@ Cumulative Update 5 (CU5) release for SQL Server Big Data Clusters.
 - Updated the BDC deployment security model so privileged containers deployed as part of BDC are no longer *required*. In addition to non-privileged, containers are running as non-root user by default for all new deployments using SQL Server Big Data Clusters CU5.
 - Added support for deploying multiple big data clusters against an Active Directory domain.
 - [!INCLUDE[azure-data-cli-azdata](../includes/azure-data-cli-azdata.md)] has its own semantic version, independent from the server. Any dependency between the client and the server version of azdata is removed. We recommend using the latest version for both client and server to ensure you are benefiting from latest enhancements and fixes.
-- Introduced two new stored procedures, `sp_data_source_objects` and `sp_data_source_table_columns`, to support introspection of certain External Data Sources. They can be used by customers directly via T-SQL for schema discovery and to see what tables are available to be virtualized. We leverage these changes in the External Table Wizard of the [Data Virtualization Extension](../azure-data-studio/extensions/data-virtualization-extension.md) for  Azure Data Studio, which allows you to create external tables from SQL Server, Oracle, MongoDB, and Teradata.
+- Introduced two new stored procedures, `sp_data_source_objects` and `sp_data_source_table_columns`, to support introspection of certain External Data Sources. They can be used by customers directly via T-SQL for schema discovery and to see what tables are available to be virtualized. We leverage these changes in the External Table Wizard of the [Data Virtualization Extension](/azure-data-studio/extensions/data-virtualization-extension) for  Azure Data Studio, which allows you to create external tables from SQL Server, Oracle, MongoDB, and Teradata.
 - Added support to persist customizations performed in Grafana. Before CU5, customers would notice that any edits in Grafana configurations would be lost upon `metricsui` pod (that hosts Grafana dashboard) restart. This issue is fixed and all configurations are now persisted.
 - Fixed security issue related to the API used to collect pod and node metrics using Telegraf (hosted in the `metricsdc` pods). As a result of this change, Telegraf now requires a service account, cluster role, and cluster bindings to have the necessary permissions to collect the pod and node metrics. See [Custer role required for pods and nodes metrics collection](kubernetes-rbac.md#cluster-role-required-for-pods-and-nodes-metrics-collection) for more details.
 - Added two feature switches to control the collection of pod and node metrics. In case you are using different solutions for monitoring your Kubernetes infrastructure, you can turn off the built-in metrics collection for pods and host nodes by setting *allowNodeMetricsCollection* and *allowPodMetricsCollection* to false in control.json deployment configuration file. For OpenShift environments, these settings are set to false by default in the built-in deployment profiles, since collecting pod and node metrics required privileged capabilities.

docs/big-data-cluster/spark-create-machine-learning-model.md

@@ -48,7 +48,7 @@ For this sample, census data (**AdultCensusIncome.csv**) is used to build a Spar
 
    This notebook contains cells with the required commands for this section of the sample.
 
-1. Open the notebook in Azure Data Studio, and run each code block. For more information about working with notebooks, see [How to use notebooks with SQL Server](../azure-data-studio/notebooks/notebooks-guidance.md).
+1. Open the notebook in Azure Data Studio, and run each code block. For more information about working with notebooks, see [How to use notebooks with SQL Server](/azure-data-studio/notebooks/notebooks-guidance).
 
 The data is first read into Spark and split into training and testing data sets. Then the code trains a pipeline model with the training data. Finally, it exports the model to an MLeap bundle.
 

docs/connect/ado-net/populate-dataset-from-dataadapter.md

@@ -46,7 +46,7 @@ If the `DataAdapter` encounters multiple result sets, it creates multiple tables
 
 ## Populate a DataSet from multiple DataAdapters  
 
-Any number of `DataAdapter` objects can be used with a `DataSet`. Each `DataAdapter` can be used to fill one or more `DataTable` objects and resolve updates back to the relevant data source. `DataRelation` and `Constraint` objects can be added to the `DataSet` locally, which enables you to relate data from dissimilar data sources. For example, a `DataSet` can contain data from a Microsoft SQL Server database, an IBM DB2 database exposed through OLE DB, and a data source that streams XML. One or more `DataAdapter` objects can handle communication to each data source.  
+Any number of `DataAdapter` objects can be used with a `DataSet`. Each `DataAdapter` can be used to fill one or more `DataTable` objects and resolve updates back to the relevant data source. `DataRelation` and `Constraint` objects can be added to the `DataSet` locally, which enables you to relate data from dissimilar data sources. For example, a `DataSet` can contain data from a Microsoft SQL Server database, an IBM Db2 database exposed through OLE DB, and a data source that streams XML. One or more `DataAdapter` objects can handle communication to each data source.  
 
 ### Example