Merge pull request #6216 from MicrosoftDocs/main

9/4/2024 AM Publish

data-explorer/.openpublishing.redirection.json

@@ -375,6 +375,11 @@
       "redirect_url": "/azure/data-explorer/get-data-storage",
       "redirect_document_id": false
     },
+    {
+      "source_path": "python-package-reference.md",
+      "redirect_url": "/kusto/query/python-package-reference?view=azure-data-explorer&preserve-view=true",
+      "redirect_document_id": false
+    },
     {
       "source_path": "engine-v3.md",
       "redirect_url": "/azure/data-explorer/how-it-works",

data-explorer/.openpublishing.redirection.migrated.json

@@ -4517,7 +4517,7 @@
     },
     {
       "source_path_from_root": "/data-explorer/kusto-redirections/api/powershell/azure-powershell.md",
-      "redirect_url": "/kusto/api/powershell/azure-powershell?view=azure-data-explorer&preserve-view=true",
+      "redirect_url": "/azure/data-explorer/azure-powershell",
       "redirect_document_id": false
     },
     {
@@ -7667,12 +7667,12 @@
     },
     {
       "source_path_from_root": "/data-explorer/kusto-redirections/tools/logicapps.md",
-      "redirect_url": "/kusto/tools/logicapps?view=azure-data-explorer&preserve-view=true",
+      "redirect_url": "/azure/data-explorer/logic-apps",
       "redirect_document_id": false
     },
     {
       "source_path_from_root": "/data-explorer/kusto-redirections/tools/synckusto.md",
-      "redirect_url": "/kusto/tools/synckusto?view=azure-data-explorer&preserve-view=true",
+      "redirect_url": "/azure/data-explorer/sync-kusto",
       "redirect_document_id": false
     }
   ]

data-explorer/kusto/api/powershell/azure-powershell.md → data-explorer/azure-powershell.md

@@ -8,8 +8,6 @@ ms.date: 08/11/2024
 ---
 # Use Kusto cmdlets in Azure PowerShell
 
-> [!INCLUDE [applies](../../includes/applies-to-version/applies.md)] [!INCLUDE [fabric](../../includes/applies-to-version/fabric.md)] [!INCLUDE [azure-data-explorer](../../includes/applies-to-version/azure-data-explorer.md)]
-
 PowerShell scripts can use Azure PowerShell [Az.Kusto cmdlets](/powershell/module/az.kusto) to run management commands.
 
 The steps in this article aren't required if you're running commands in [Azure Cloud Shell](https://shell.azure.com). If you're running the CLI locally, follow these steps to set up your environment.
@@ -59,4 +57,4 @@ For a list of all available cmdlets, see [Az.Kusto cmdlets](/powershell/module/a
 
 ## Related content
 
-* [Use Kusto .NET client libraries from PowerShell](powershell.md)
+* [Use Kusto .NET client libraries from PowerShell](/kusto/api/powershell/powershell?view=azure-data-explorer&preserve-view=true)

data-explorer/kusto/.openpublishing.redirection.json

@@ -5,6 +5,36 @@
       "redirect_url": "/kusto/concepts/sandboxes",
       "redirect_document_id": false
     },
+    {
+      "source_path": "api/powershell/azure-powershell.md",
+      "redirect_url": "/azure/data-explorer/azure-powershell",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "concepts/dealing-with-duplicates.md",
+      "redirect_url": "/azure/data-explorer/dealing-with-duplicates",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "concepts/delete-data.md",
+      "redirect_url": "/azure/data-explorer/delete-data",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "python-package-reference.md",
+      "redirect_url": "/kusto/query/python-package-reference",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "tools/logicapps.md",
+      "redirect_url": "/azure/data-explorer/logic-apps",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "tools/synckusto.md",
+      "redirect_url": "/azure/data-explorer/sync-kusto",
+      "redirect_document_id": false
+    },
     {
       "source_path": "query/re2.md",
       "redirect_url": "/kusto/query/regex",

data-explorer/kusto/concepts/data-soft-delete.md

@@ -13,15 +13,15 @@ monikerRange: "azure-data-explorer"
 The ability to delete individual records is supported. Record deletion is commonly achieved using one of the following methods:
 
 * To delete records with a system guarantee that the storage artifacts containing these records are deleted as well, use [`.purge`](data-purge.md)
-* To delete records without such a guarantee, use `.delete` as described in this article - this command marks records as deleted but doesn't necessarily delete the data from storage artifacts. This deletion method is faster than purge. 
+* To delete records without such a guarantee, use `.delete` as described in this article - this command marks records as deleted but doesn't necessarily delete the data from storage artifacts. This deletion method is faster than purge.
 
 For information on how to use the command, see [Syntax](../management/soft-delete-command.md)
 
 ## Use cases
 
 This deletion method should only be used for the unplanned deletion of individual records. For example, if you discover that an IoT device is reporting corrupt telemetry for some time, you should consider using this method to delete the corrupt data.
 
-If you need to frequently delete records for deduplication or updates, we recommend using [materialized views](../management/materialized-views/materialized-view-overview.md). See [choose between materialized views and soft delete for data deduplication](dealing-with-duplicates.md#choose-between-materialized-views-and-soft-delete-for-data-deduplication).
+If you need to frequently delete records for deduplication or updates, we recommend using [materialized views](../management/materialized-views/materialized-view-overview.md). See [choose between materialized views and soft delete for data deduplication](/azure/data-explorer/dealing-with-duplicates#choose-between-materialized-views-and-soft-delete-for-data-deduplication).
 
 ## Deletion process
 

data-explorer/kusto/includes/python-plugin-adx.md

@@ -39,7 +39,7 @@ The plugin is disabled by default. Before you start, review the list of [prerequ
 
 To change the version of the Python image, see [Change the Python language extensions image on your cluster](/azure/data-explorer/language-extensions#change-the-python-language-extensions-image-on-your-cluster).
 
-To see the list of packages for the different Python images, see [Python package reference](../python-package-reference.md).
+To see the list of packages for the different Python images, see [Python package reference](../query/python-package-reference.md).
 
 > [!NOTE]
 >

data-explorer/kusto/includes/python-plugin-fabric.md

@@ -36,7 +36,7 @@ The plugin is disabled by default. Before you start, [enable the Python plugin](
 
 ## Python sandbox image
 
-To see the list of packages for the different Python images, see [Python package reference](../python-package-reference.md).
+To see the list of packages for the different Python images, see [Python package reference](../query/python-package-reference.md).
 
 > [!NOTE]
 >

data-explorer/kusto/management/update-table-command.md

@@ -80,7 +80,7 @@ Use the following guidelines to decide which method to use:
 
 * If your update pattern isn't supported by materialized views, use the update command.
 * If the source table has a high ingestion volume, but only few updates, using the update command can be more performant and consume less cache or storage than materialized views. This is because materialized views need to reprocess all ingested data, which is less efficient than identifying the individual records to update based on the append or delete predicates.
-* Materialized views are a fully managed solution. The materialized view is [defined once](materialized-views/materialized-view-create-or-alter.md) and materialization happens in the background by the system. The update command requires an orchestrated process (for example, [Azure Data Factory](/azure/data-explorer/data-factory-integration), [Logic Apps](../tools/logicapps.md), [Power Automate](/azure/data-explorer/flow), and others) that explicitly executes the update command every time there are updates. If materialized views work enough for your use case, using materialized views requires less management and maintenance.
+* Materialized views are a fully managed solution. The materialized view is [defined once](materialized-views/materialized-view-create-or-alter.md) and materialization happens in the background by the system. The update command requires an orchestrated process (for example, [Azure Data Factory](/azure/data-explorer/data-factory-integration), [Logic Apps](/azure/data-explorer/logic-apps), [Power Automate](/azure/data-explorer/flow), and others) that explicitly executes the update command every time there are updates. If materialized views work enough for your use case, using materialized views requires less management and maintenance.
 
 ## Performance tips