diff --git a/data-explorer/.openpublishing.redirection.json b/data-explorer/.openpublishing.redirection.json
index 4fe514c58d..96581bf158 100644
--- a/data-explorer/.openpublishing.redirection.json
+++ b/data-explorer/.openpublishing.redirection.json
@@ -15,6 +15,11 @@
"redirect_url": "/kusto/query/graph-best-practices?view=azure-data-explorer&preserve-view=true",
"redirect_document_id": false
},
+ {
+ "source_path": "azure/synapse-analytics/quickstart-connect-azure-data-explorer.md",
+ "redirect_url": "/azure/data-explorer/integrate-overview",
+ "redirect_document_id": false
+ },
{
"source_path": "graph-overview.md",
"redirect_url": "/kusto/query/graph-semantics-overview?view=azure-data-explorer&preserve-view=true",
diff --git a/data-explorer/auto-stop-clusters.md b/data-explorer/auto-stop-clusters.md
index 5e0772d014..401e0f8b87 100644
--- a/data-explorer/auto-stop-clusters.md
+++ b/data-explorer/auto-stop-clusters.md
@@ -1,81 +1,79 @@
---
title: Automatic stop of inactive clusters in Azure Data Explorer
-description: Learn when your cluster will be stopped using the Automatic stop feature, and how to enable/disable the Automatic stop.
+description: Learn when your cluster is due to stop running using the Automatic stop feature, and how to enable/disable the Automatic stop.
ms.reviewer: orhasban
ms.topic: how-to
-ms.date: 11/03/2021
+ms.date: 12/08/2025
---
-# Automatic stop of inactive Azure Data Explorer clusters
+# Configure automatic stop of inactive Azure Data Explorer clusters
-Azure Data Explorer clusters that have been *inactive* for a specified time interval are automatically stopped. Inactivity is defined as clusters that haven't had any data ingestion or queries in the past 5 days. The interval is fixed at 5 days and cannot be changed.
+Azure Data Explorer clusters that are *inactive* for a specified time interval stop automatically. Inactivity means clusters with no data ingestion or queries in the past 5 days. The interval is fixed at 5 days and can't be changed.
-Cluster behavior isn't automatically resumed. To restart the cluster, do so manually.
+Cluster behavior doesn't resume automatically. Restart the cluster manually.
> [!NOTE]
-> Cluster types listed below are not automatically stopped:
+> The following cluster types aren't stopped automatically:
>
> * Leader clusters. For more information, see [follower databases](follower.md).
> * Clusters deployed in a Virtual Network
> * [Start-for-free](start-for-free.md) clusters
-> * Clusters where the [Auto-Stop setting](auto-stop-clusters.md#set-auto-stop-settings-while-creating-a-new-cluster) is turned off
-> * Azure Synapse Data Explorer pools
+> * Clusters where the [Auto-Stop setting](auto-stop-clusters.md#configure-auto-stop-while-creating-a-new-cluster) is turned off
-## Manage Automatic stop behavior on your cluster
+## Manage automatic stop behavior on your cluster
-Azure Data Explorer clusters are created by default with the cluster property of `enableAutoStop = true`. This property can be set or altered either on cluster creation or post creation.
+Azure Data Explorer clusters are created by default with the cluster property `enableAutoStop = true`. You can set or change this property during or after cluster creation.
+
+[Azure portal](#configure-auto-stop-while-creating-a-new-cluster)
-Set this property using one of the following methods, or using the [Azure portal](#azure-portal):
* [ARM Templates](/azure/templates/microsoft.kusto/clusters?tabs=json#trustedexternaltenant-object)
* [Azure CLI](/cli/azure/kusto/cluster#az-kusto-cluster-update-optional-parameters)
* [PowerShell](/powershell/module/az.kusto/new-azkustocluster)
-* [Azure Resource Explorer](https://resources.azure.com/).
-
-For more information, see [Azure Data Explorer cluster request body](/rest/api/azurerekusto/clusters/createorupdate#request-body).
+* [Azure Resource Explorer](https://resources.azure.com/)
-## Azure portal
+Learn more in [Azure Data Explorer cluster request body](/rest/api/azurerekusto/clusters/createorupdate#request-body).
-### Set Auto-Stop settings while creating a new cluster
+## Configure auto-stop while creating a new cluster
-1. Follow the steps in [Create an Azure Data Explorer cluster and database](create-cluster-and-database.md).
+1. In the Azure portal, follow the steps in [create an Azure Data Explorer cluster and database](create-cluster-and-database.md).
1. In the **Configurations** tab, select **Auto-Stop cluster** > **On**.
-:::image type="content" source="media/auto-stop-clusters/auto-stop-cluster-creation.png" alt-text="Screenshot of auto-stop configuration.":::
+ :::image type="content" source="media/auto-stop-clusters/auto-stop-cluster-creation.png" alt-text="Screenshot of auto-stop configuration." :::
-### Modify settings on an existing cluster
+## Modify settings on an existing cluster
-To enable/disable Auto-Stop cluster after cluster was created:
+To enable or disable Auto-Stop cluster after the cluster is created:
-1. Sign into the [Azure portal](https://ms.portal.azure.com/).
-1. Browse to your Azure Data Explorer cluster.
+1. Sign in to the [Azure portal](https://ms.portal.azure.com/).
+1. Go to your Azure Data Explorer cluster.
1. In **Settings**, select **Configurations**.
-1. In the **Configurations** pane, select **On**/**Off** to enable/disable **Auto-Stop cluster**.
+1. In the **Configurations** pane, select **On** or **Off** to enable or disable **Auto-Stop cluster**.
1. Select **Save**.
-:::image type="content" source="media/auto-stop-clusters/auto-stop-cluster-update.png" alt-text="Screenshot of auto-stop configuration in Azure portal.":::
+:::image type="content" source="media/auto-stop-clusters/auto-stop-cluster-update.png" alt-text="Screenshot of auto-stop configuration in the Azure portal.":::
-## Verify Auto-Stop using the Activity log
+## Verify auto-stop using the activity log
When a cluster is automatically stopped, an Activity log is sent. To verify when and how the cluster was stopped, use the following steps:
1. Sign into the [Azure portal](https://ms.portal.azure.com/).
1. Browse to Azure Data Explorer cluster.
-1. On the left pane, select **Activity log**.
-1. Choose a timespan.
-1. Under **Operation name**, look for **Stop Clusters**.
+1. In the left pane, select **Activity log**.
+1. Select a timespan.
+1. Under **Operation name**, find **Stop Clusters**.
-:::image type="content" source="media/auto-stop-clusters/auto-stop-cluster-activity-log.png" alt-text="Screenshot of activity log.":::
+:::image type="content" source="media/auto-stop-clusters/auto-stop-cluster-activity-log.png" alt-text="Screenshot of the activity log.":::
## Examples
### REST example
-Update the cluster using the following operation:
+Update the cluster with this operation:
```http
PATCH https://management.azure.com/subscriptions/12345678-1234-1234-1234-123456789098/resourceGroups/kustorgtest/providers/Microsoft.Kusto/clusters/kustoclustertest?api-version=2021-08-27
```
-#### Request body to disable Auto-Stop
+#### Request body to disable auto-stop
```json
{
@@ -85,12 +83,12 @@ PATCH https://management.azure.com/subscriptions/12345678-1234-1234-1234-1234567
}
```
-#### Request body to enable Auto-Stop
+#### Request body to enable auto-stop
```json
{
- "properties": {
- "enableAutoStop": true
- }
+ "properties": {
+ "enableAutoStop": true
+ }
}
```
diff --git a/data-explorer/azure-advisor.md b/data-explorer/azure-advisor.md
index ef5bbc0140..00d6bcabee 100644
--- a/data-explorer/azure-advisor.md
+++ b/data-explorer/azure-advisor.md
@@ -8,9 +8,9 @@ ms.date: 03/08/2023
# Use Azure Advisor recommendations to optimize your Azure Data Explorer cluster
-Azure Advisor analyzes the Azure Data Explorer cluster configurations and usage telemetry and offers personalized and actionable recommendations to help you optimize your cluster.
+Azure Advisor analyzes Azure Data Explorer cluster configurations and usage telemetry, and offers personalized, actionable recommendations to help you optimize your cluster.
-## Access the Azure Advisor recommendations
+## Access Azure Advisor recommendations
There are two ways to access the Azure Advisor recommendations:
@@ -19,45 +19,45 @@ There are two ways to access the Azure Advisor recommendations:
### View Azure Advisor recommendations for your Azure Data Explorer cluster
-1. In the Azure portal, go to your Azure Data Explorer cluster page.
-1. In the left-hand menu, under **Monitoring**, select **Advisor recommendations**. A list of recommendations opens for that cluster.
+1. In the Azure portal, go to your Azure Data Explorer cluster page.
+1. In the left menu, under **Monitoring**, select **Advisor recommendations**. A list of recommendations opens for that cluster.
- :::image type="content" source="media/azure-advisor/resource-group-advisor-recommendations.png" alt-text="Azure Advisor recommendations for your Azure Data Explorer cluster.":::
+ :::image type="content" source="media/azure-advisor/resource-group-advisor-recommendations.png" alt-text="Screenshot of Azure Advisor recommendations for an Azure Data Explorer cluster.":::
### View Azure Advisor recommendations for all clusters in your subscription
-1. In the Azure portal, go to the [Advisor resource](https://ms.portal.azure.com/#blade/Microsoft_Azure_Expert/AdvisorMenuBlade/overview).
-1. In **Overview**, select one or more subscriptions for which you want recommendations.
+1. In the Azure portal, go to the [Advisor resource](https://ms.portal.azure.com/#blade/Microsoft_Azure_Expert/AdvisorMenuBlade/overview).
+1. In **Overview**, select one or more subscriptions to get recommendations.
1. Select **Azure Data Explorer Clusters** and **Azure Data Explorer Databases** in the second dropdown.
-
- :::image type="content" source="media/azure-advisor/advisor-resource.png" alt-text="Azure Advisor resource.":::
-## Use the Azure Advisor recommendations
+ :::image type="content" source="media/azure-advisor/advisor-resource.png" alt-text="Screenshot of Azure Advisor resource page.":::
-There are various Azure Advisor recommendation types. Use the relevant recommendation type to help you optimize your cluster.
+## Use Azure Advisor recommendations
-1. In **Advisor**, under **Recommendations**, select **Cost** for cost recommendations.
+Azure Advisor offers different recommendation types. Use the relevant type to optimize your cluster.
- :::image type="content" source="media/azure-advisor/select-recommendation-type.png" alt-text="Select recommendation type.":::
+1. In **Advisor**, under **Recommendations**, select **Cost** to view cost recommendations.
+
+ :::image type="content" source="media/azure-advisor/select-recommendation-type.png" alt-text="Screenshot of the Azure Advisor interface showing the selection of recommendation type.":::
1. Select a recommendation from the list.
- :::image type="content" source="media/azure-advisor/select-recommendation.png" alt-text="Select recommendation.":::
+ :::image type="content" source="media/azure-advisor/select-recommendation.png" alt-text="Screenshot of the Azure Advisor interface showing a list of recommendations.":::
-1. The following window contains a list of clusters to which the recommendation is relevant. The recommendation details are different for every cluster and include the recommended action.
+1. The window shows a list of clusters relevant to the recommendation. Recommendation details vary for each cluster and include the recommended action.
- :::image type="content" source="media/azure-advisor/clusters-with-recommendations.png" alt-text="List of clusters with recommendations.":::
+ :::image type="content" source="media/azure-advisor/clusters-with-recommendations.png" alt-text="Screenshot showing a list of clusters with relevant recommendations in Azure Advisor.":::
## Recommendation types
-Cost, performance, reliability, and service excellence recommendations are currently available.
+Cost, performance, reliability, and service excellence recommendations are available.
> [!IMPORTANT]
-> Your actual yearly savings may vary. The yearly savings presented are based on 'pay-as-you-go' prices. These potential saving don't take into account Azure Reserved Virtual Machine Instance (RIs) billing discounts.
+> Your actual yearly savings may vary. The yearly savings presented are based on 'pay-as-you-go' prices. These potential savings don't take into account Azure Reserved Virtual Machine Instance (RIs) billing discounts.
### Cost recommendations
-The **Cost** recommendations are available for clusters that can be changed to reduce cost without compromising performance.
+The **Cost** recommendations are for clusters that can be changed to reduce cost without compromising performance.
Cost recommendations include:
* [Unused running Azure Data Explorer cluster](#unused-running-azure-data-explorer-cluster)
@@ -70,12 +70,12 @@ Cost recommendations include:
A cluster is considered unused and running if it is in the running state and has neither ingested data nor run queries in the past five days.
In some cases, clusters may be [automatically stopped](auto-stop-clusters.md). In the following cases, the cluster won't automatically stop and a recommendation will be shown:
+
* Leader clusters. For more information, see [follower databases](follower.md).
* Clusters deployed in a Virtual Network.
- * Clusters where the [Auto-Stop setting](auto-stop-clusters.md#set-auto-stop-settings-while-creating-a-new-cluster) is turned off
- * Azure Synapse Data Explorer pools
-
-The recommendation is to stop the cluster to reduce cost but still preserve the data. If the data isn't needed, consider deleting the cluster to increase your savings.
+ * Clusters where the [Auto-Stop setting](auto-stop-clusters.md#configure-auto-stop-while-creating-a-new-cluster) is turned off
+
+The recommendation is to stop the cluster to reduce cost while preserving the data. If the data isn't needed, consider deleting the cluster to increase your savings.
#### Unused stopped Azure Data Explorer cluster
@@ -86,23 +86,22 @@ The recommendation is to delete the cluster to reduce cost.
> [!CAUTION]
> Stopped clusters may still contain data. Before deleting the cluster, verify that the data is no longer needed. Once the cluster is deleted, the data will no longer be accessible.
-
#### Change Data Explorer clusters to a more cost effective and better performing SKU
-The recommendation **Change Data Explorer clusters to a more cost effective and better performing SKU** is given to a cluster whose cluster is operating under a non-optimal SKU. This updated SKU should reduce your costs and improve overall performance. We have calculated the required instance count that meets the cache requirements of your cluster, while ensuring that performance will not be negatively impacted.
+The recommendation **Change Data Explorer clusters to a more cost effective and better performing SKU** is for a cluster operating under a nonoptimal SKU. This updated SKU should reduce your costs and improve overall performance. We have calculated the required instance count that meets the cache requirements of your cluster, while ensuring that performance won't be negatively impacted.
-As part of the recommendation, we recommend enabling Optimized Autoscale if not yet enabled. Optimized Autoscale will perform a more in-depth analysis of the cluster's performance, and if needed, will further scale-in the cluster. This will result in additional cost reductions. The Optimized Autoscale recommendations include a Min and Max instance count recommendation. The Max value is set to the recommended SKU instance count. If the cluster has plans to organically grow, it is recommended to manually increase this Max number. If Optimized Autoscale is already configured on your cluster, in some cases the recommendation may suggest to increase the Max instance count.
+As part of the recommendation, we recommend enabling Optimized Autoscale if not yet enabled. Optimized Autoscale will perform a more in-depth analysis of the cluster's performance, and if needed, will further scale-in the cluster. This results in more cost reductions. The Optimized Autoscale recommendations include minimum and maximum instance count recommendations. The Max value is set to the recommended SKU instance count. If the cluster has plans to organically grow, it's recommended to manually increase this Max number. If Optimized Autoscale is already configured on your cluster, in some cases the recommendation may suggest increasing the Max instance count.
-The SKU recommendation takes into account the current zones definitions of a cluster and if the cluster supports zones will only recommend target SKUs that have a minimum of two zones. Adding more compute availability zones does not incur any additional costs.
+The SKU recommendation takes into account the current zones definitions of a cluster and if the cluster supports zones will only recommend target SKUs that have a minimum of two zones. Adding more compute availability zones doesn't incur extra costs.
-The advisor SKU recommendation is updated every few hours. The recommendation checks for capacity availability of the selected SKU in the region. However, it is important to note that capacity availability is dynamic and changes over time.
+The advisor SKU recommendation is updated every few hours. The recommendation checks for capacity availability of the selected SKU in the region. However, it's important to note that capacity availability is dynamic and changes over time.
> [!NOTE]
-> The advisor SKU recommendation does not currently support clusters with Virtual Network or managed private endpoint configurations.
+> The advisor SKU recommendation doesn't support clusters with Virtual Network or managed private endpoint configurations.
#### Reduce cache for Azure Data Explorer tables
-The **reduce Azure Data Explorer table cache period for cluster cost optimization** recommendation is given for a cluster that can reduce its table's [cache policy](/kusto/management/cache-policy?view=azure-data-explorer&preserve-view=true). This recommendation is based on the query look-back period during the last 30 days. To see where savings are possible, you can view the most relevant 5 tables per database for potential cache savings. This recommendation is only offered if the cluster can scale-in or scale-down after a cache policy change. Advisor checks if the cluster is "bounded by data", meaning the cluster has low CPU and low ingestion utilization, but because of high data capacity the cluster can't scale-in or scale-down.
+The **reduce Azure Data Explorer table cache period for cluster cost optimization** recommendation is given for a cluster that can reduce its table's [cache policy](/kusto/management/cache-policy?view=azure-data-explorer&preserve-view=true). This recommendation is based on the query look-back period during the last 30 days. To see where savings are possible, you can view the most relevant five tables per database for potential cache savings. This recommendation is only offered if the cluster can scale-in or scale-down after a cache policy change. Advisor checks if the cluster is "bounded by data," meaning the cluster has low CPU and low ingestion utilization, but because of high data capacity the cluster can't scale-in or scale-down.
#### Enable Optimized autoscale
@@ -111,20 +110,20 @@ The recommendation **enable Optimized autoscale** is given when enabling [Optimi
### Performance recommendations
-The **Performance** recommendations help improve the performance of your Azure Data Explorer clusters.
+The **Performance** recommendations improve the performance of your Azure Data Explorer clusters.
Performance recommendations include the following:
* [Change Data Explorer clusters to a more cost effective and better performing SKU](#change-data-explorer-clusters-to-a-more-cost-effective-and-better-performing-sku)
* [Update the cache policy for Azure Data Explorer tables](#update-cache-policy-for-azure-data-explorer-tables)
#### Update cache policy for Azure Data Explorer tables
-The **review Azure Data Explorer table cache-period policy for better performance** recommendation is given for a cluster that requires a different look-back period time filter, or a larger [cache policy](/kusto/management/cache-policy?view=azure-data-explorer&preserve-view=true). This recommendation is based on the query look-back period of the last 30 days. Most queries run in the last 30 days accessed data not in the cache, which can increase the query run-time. You can view the top 5 tables per database that accessed out-of-cache data, ordered by querying percentage.
+The **review Azure Data Explorer table cache-period policy for better performance** recommendation is given for a cluster that requires a different look-back period time filter, or a larger [cache policy](/kusto/management/cache-policy?view=azure-data-explorer&preserve-view=true). This recommendation is based on the query look-back period of the last 30 days. Most queries run in the last 30 days accessed data not in the cache, which can increase the query run-time. You can view the top five tables per database that accessed out-of-cache data, ordered by querying percentage.
-You may also get a performance recommendation to reduce the cache policy. This can happen if the cluster is data-bound. A cluster is data-bound if the data to be cached according to the caching policy is larger that the total size of the cluster's cache. Reducing the cache policy for data-bound clusters will reduce the number of cache misses and potentially improves performance.
+You may also get a performance recommendation to reduce the cache policy. This can happen if the cluster is data-bound. A cluster is data-bound if the data to be cached according to the caching policy is larger that the total size of the cluster's cache. Reducing the cache policy for data-bound clusters reduce the number of cache misses and potentially improves performance.
### Operational Excellence recommendations
-The **Operational Excellence** or "best practice" recommendations are recommendations whose implementation does not improve cost or performance immediately but can benefit the cluster in the future. This includes [reducing the table cache policy to match usage patterns](#reduce-table-cache-policy-to-match-usage-patterns).
+The **Operational Excellence** or "best practice" recommendations are recommendations whose implementation doesn't improve cost or performance immediately but can benefit the cluster in the future. This includes [reducing the table cache policy to match usage patterns](#reduce-table-cache-policy-to-match-usage-patterns).
#### Reduce table cache policy to match usage patterns
@@ -133,7 +132,8 @@ This recommendation can be useful for tables where the actual query lookback bas
### Reliability recommendations
-The **Reliability recommendations** help you ensure and improve the continuity of your business-critical applications.
+The **Reliability recommendations** help you ensure and improve the continuity of your business-critical applications.
+
Reliability recommendations include the following:
* [Cluster uses subnet without delegation](#cluster-uses-subnet-without-delegation)
@@ -141,7 +141,7 @@ Reliability recommendations include the following:
#### Cluster uses subnet without delegation
-The strong recommendation is given to a virtual network cluster that uses a subnet without delegation for 'Microsoft.Kusto/clusters'. When you delegate a subnet to a cluster, you allow that service to establish basic network configuration rules for the subnet, which helps the cluster operate its instances in a stable manner.
+This recommendation is for a virtual network cluster that uses a subnet without delegation for 'Microsoft.Kusto/clusters'. When you delegate a subnet to a cluster, you allow that service to establish basic network configuration rules for the subnet, which helps the cluster operate its instances in a stable manner.
#### Cluster uses subnet with invalid IP configuration
@@ -150,4 +150,4 @@ The recommendation is given to a virtual network cluster where the subnet is als
## Related content
* [Manage cluster horizontal scaling (scale out) in Azure Data Explorer to accommodate changing demand](manage-cluster-horizontal-scaling.md)
-* [Manage cluster vertical scaling (scale up) in Azure Data Explorer to accommodate changing demand](manage-cluster-vertical-scaling.md)
+* [Manage cluster vertical scaling (scale up) in Azure Data Explorer to accommodate changing demand](manage-cluster-vertical-scaling.md).
diff --git a/data-explorer/data-explorer-insights.md b/data-explorer/data-explorer-insights.md
index ab112ddd84..8adc6ff7ae 100644
--- a/data-explorer/data-explorer-insights.md
+++ b/data-explorer/data-explorer-insights.md
@@ -2,7 +2,7 @@
title: Azure Data Explorer Clusters insights
description: This article describes how to use Azure Data Explorer Clusters Insights.
ms,reviewer: guregini
-ms.topic: conceptual
+ms.topic: article
ms.date: 05/24/2022
ms.custom:
- subject-monitoring
@@ -123,7 +123,7 @@ The **Tables** tab shows the latest and historical properties of tables in the c
The **Cache** tab allows users to analyze their actual queries' lookback window patterns and compare them to the configured cache policy (for each table). You can identify tables used by the most queries and tables that aren't queried at all, and adapt the cache policy accordingly.
-You might get cache policy recommendations on specific tables in Azure Advisor. Currently, cache recommendations are available only from the [main Azure Advisor dashboard](azure-advisor.md#use-the-azure-advisor-recommendations). They're based on actual queries' lookback window in the past 30 days and an unoptimized cache policy for at least 95 percent of the queries.
+You might get cache policy recommendations on specific tables in Azure Advisor. Currently, cache recommendations are available only from the [main Azure Advisor dashboard](azure-advisor.md#use-azure-advisor-recommendations). They're based on actual queries' lookback window in the past 30 days and an unoptimized cache policy for at least 95 percent of the queries.
Cache reduction recommendations in Azure Advisor are available for clusters that are "bounded by data." That means the cluster has low CPU and low ingestion utilization, but because of high data capacity, the cluster can't scale in or scale down.
@@ -183,7 +183,7 @@ The following sections will help you diagnose and troubleshoot of some of the co
### Why don't I see all my subscriptions in the subscription picker?
- shows only subscriptions that contain Azure Data Explorer clusters chosen from the selected subscription filter. You select a subscription filter under **Directory + subscription** in the Azure portal.
+Shows only subscriptions that contain Azure Data Explorer clusters chosen from the selected subscription filter. You select a subscription filter under **Directory + subscription** in the Azure portal.
:::image type="content" source="/azure/azure-monitor/insights/media/key-vaults-insights-overview/Subscriptions.png" alt-text="Screenshot of selecting a subscription filter.":::
diff --git a/data-explorer/database-script.md b/data-explorer/database-script.md
index ddc1255989..d7049c1386 100644
--- a/data-explorer/database-script.md
+++ b/data-explorer/database-script.md
@@ -268,7 +268,7 @@ Use the following settings:
## Limitations
-* Scripts are only supported in Azure Data Explorer; Scripts aren't supported in Synapse Data Explorer pools.
+* Scripts are only supported in Azure Data Explorer.
* Two scripts can't be added, modified, or removed in parallel on the same cluster. If this occurs, the following error: `Code="ServiceIsInMaintenance"` is raised. You can work around the issue by placing a dependency between the two scripts so that they're created or updated sequentially.
* To create functions with [cross-cluster queries](/kusto/query/cross-cluster-or-database-queries?view=azure-data-explorer&preserve-view=true) using scripts, you must set the `skipvalidation` property to `true` in the [.create function command](/kusto/management/create-function?view=azure-data-explorer&preserve-view=true).
diff --git a/data-explorer/ingest-data-event-hub-overview.md b/data-explorer/ingest-data-event-hub-overview.md
index 23cd67a3da..9cedea10f3 100644
--- a/data-explorer/ingest-data-event-hub-overview.md
+++ b/data-explorer/ingest-data-event-hub-overview.md
@@ -1,9 +1,9 @@
---
-title: Ingest from Event Hub - Azure Data Explorer
+title: Ingest from Event Hubs - Azure Data Explorer
description: This article describes how to ingest data from Azure Event Hubs into Azure Data Explorer.
ms.reviewer: orspodek
ms.topic: how-to
-ms.date: 08/26/2025
+ms.date: 12/01/2025
ms.custom: sfi-ropc-nochange
---
# Azure Event Hubs data connection
@@ -214,7 +214,7 @@ See the [sample app](https://github.com/Azure-Samples/event-hubs-dotnet-ingest)
## Set up Geo-disaster recovery solution
-Event hub offers a [Geo-disaster recovery](/azure/event-hubs/event-hubs-geo-dr) solution.
+Event hubs offer a [Geo-disaster recovery](/azure/event-hubs/event-hubs-geo-dr) solution.
Azure Data Explorer doesn't support `Alias` event hub namespaces. To implement the Geo-disaster recovery in your solution, create two event hub data connections: one for the primary namespace and one for the secondary namespace. Azure Data Explorer listens to both event hub connections.
> [!NOTE]
diff --git a/data-explorer/integrate-data-overview.md b/data-explorer/integrate-data-overview.md
index 3f1adf13ab..b75ca90694 100644
--- a/data-explorer/integrate-data-overview.md
+++ b/data-explorer/integrate-data-overview.md
@@ -27,7 +27,7 @@ Use the following filters to see other connectors, tools, and integrations are a
:::column-end:::
:::row-end:::
-The following tables summarizes the available data connectors, tools, and integrations.
+The following tables summarize the available data connectors, tools, and integrations.
## [Connectors](#tab/connectors)
@@ -37,7 +37,6 @@ The following tables summarizes the available data connectors, tools, and integr
| [Apache Flink](integrate-overview.md#apache-flink) | **Ingestion** | :heavy_check_mark: | | [Open source](https://github.com/Azure/flink-connector-kusto/) | Telemetry |
| [Apache Log4J 2](integrate-overview.md#apache-log4j-2) | **Ingestion** | :heavy_check_mark: | :heavy_check_mark: | First party, [Open source](https://github.com/Azure/azure-kusto-log4j) | Logs |
| [Apache Spark](integrate-overview.md#apache-spark) | **Export**
**Ingestion** | | | [Open source](https://github.com/Azure/azure-kusto-spark/) | Telemetry |
-| [Apache Spark for Azure Synapse Analytics](integrate-overview.md#apache-spark-for-azure-synapse-analytics) | **Export**
**Ingestion** | | | First party | Telemetry |
| [Azure Cosmos DB](integrate-overview.md#azure-cosmos-db) | **Ingestion** | :heavy_check_mark: | | First party | Change feed |
| [Azure Data Factory](integrate-overview.md#azure-data-factory) | **Export**
**Ingestion** | | | First party | Data orchestration |
| [Azure Event Grid](integrate-overview.md#azure-event-grid) | **Ingestion** | :heavy_check_mark: | | First party | Event processing |
diff --git a/data-explorer/integrate-overview.md b/data-explorer/integrate-overview.md
index b9fdb39416..9657796c71 100644
--- a/data-explorer/integrate-overview.md
+++ b/data-explorer/integrate-overview.md
@@ -2,8 +2,8 @@
title: Integrations overview
description: Learn about the available data connectors, tools, and integrations, and their capabilities.
ms.reviewer: aksdi
-ms.topic: conceptual
-ms.date: 01/16/2024
+ms.topic: article
+ms.date: 12/01/2025
---
# Integrations overview
@@ -40,7 +40,6 @@ The following table summarizes the available connectors and their capabilities:
| [Apache Flink](#apache-flink) | :heavy_check_mark: | | | |
| [Apache Log4J 2](#apache-log4j-2) | :heavy_check_mark: | | | |
| [Apache Spark](#apache-spark) | :heavy_check_mark: | :heavy_check_mark: | | :heavy_check_mark: |
-| [Apache Spark for Azure Synapse Analytics](#apache-spark-for-azure-synapse-analytics) | :heavy_check_mark: | :heavy_check_mark: | | :heavy_check_mark: |
| [Azure Cosmos DB](#azure-cosmos-db) | :heavy_check_mark: | | | |
| [Azure Data Factory](#azure-data-factory) | :heavy_check_mark: | | :heavy_check_mark: | |
| [Azure Event Grid](#azure-event-grid) | | | :heavy_check_mark: | |
@@ -72,7 +71,6 @@ The following table summarizes the available tools and integrations and their ca
| Name | Ingest | Query | Share | Source control | Secure | Administrate | Visualize |
| ----------------------------------------------------------------------------------- | ------------------ | ------------------ | ------------------ | ------------------ | ------------------ | ------------------ | ------------------ |
| [Azure CLI](#azure-cli) | | | | | | :heavy_check_mark: | |
-| [Azure Synapse Analytics](#azure-synapse-analytics) | :heavy_check_mark: | :heavy_check_mark: | | | | | :heavy_check_mark: |
| [Azure Data Lake](#azure-data-lake) | :heavy_check_mark: | :heavy_check_mark: | | | | | |
| [Azure Data Studio](#azure-data-studio) | | :heavy_check_mark: | | | | | |
| [Azure Data Share](#azure-data-share) | | | :heavy_check_mark: | | | | |
@@ -147,16 +145,6 @@ The following are detailed descriptions of connectors and tools and integrations
* **Documentation:** [Apache Spark connector](spark-connector.md)
* **Community Blog:** [Data preprocessing for Azure Data Explorer for Azure Data Explorer with Apache Spark](https://techcommunity.microsoft.com/t5/azure-data-explorer-blog/data-pre-processing-for-azure-data-explorer-with-apache-spark/ba-p/2727993/)
-### Apache Spark for Azure Synapse Analytics
-
-[Apache Spark](https://spark.apache.org/) is a parallel processing framework that supports in-memory processing to boost the performance of big data analytic applications. [Apache Spark in Azure Synapse](/azure/synapse-analytics/spark/apache-spark-overview) Analytics is one of Microsoft's implementations of Apache Spark in the cloud. You can access a database from [Synapse Studio](/azure/synapse-analytics/) with Apache Spark for Azure Synapse Analytics.
-
-* **Functionality:** Ingestion, Export
-* **Ingestion type supported:** Batching
-* **Use cases:** Telemetry
-* **Underlying SDK:** [Java](/kusto/api/java/kusto-java-client-library?view=azure-data-explorer&preserve-view=true)
-* **Documentation:** [Connect to an Azure Synapse workspace](/azure/synapse-analytics/quickstart-connect-azure-data-explorer)
-
### Azure Cosmos DB
The [Azure Cosmos DB](/azure/cosmos-db/) change feed data connection is an ingestion pipeline that listens to your Cosmos DB change feed and ingests the data into your database.
@@ -394,13 +382,6 @@ Azure CLI lets you manage Kusto resources.
* **Functionality:** Administration
* **Documentation:** [az kusto](/cli/azure/kusto?view=azure-cli-latest&preserve-view=true)
-### Azure Synapse Analytics
-
-Azure Synapse Data Explorer provides customers with an interactive query experience to unlock insights from log and telemetry data. To complement existing SQL and Apache Spark analytics runtime engines, the Data Explorer analytics runtime is optimized for efficient log analytics using powerful indexing technology to automatically index free-text and semi-structured data commonly found in telemetry data.
-
-* **Functionality:** Ingestion, Query, Visualization
-* **Documentation:** [What is Azure Synapse Data Explorer?](/azure/synapse-analytics/data-explorer/data-explorer-overview)
-
### Azure Data Lake
Azure Data Explorer integrates with Azure Blob Storage and Azure Data Lake Storage (Gen1 and Gen2), providing fast, cached, and indexed access to data stored in external storage.
diff --git a/data-explorer/integrate-query-overview.md b/data-explorer/integrate-query-overview.md
index 6a37233766..1f561b2f9d 100644
--- a/data-explorer/integrate-query-overview.md
+++ b/data-explorer/integrate-query-overview.md
@@ -2,8 +2,8 @@
title: Query integrations overview
description: Learn about the available query integrations.
ms.reviewer: aksdi
-ms.topic: conceptual
-ms.date: 01/30/2024
+ms.topic: article
+ms.date: 12/01/2025
# CustomerIntent: As a data ingestor, I want to know what query connectors and tools are available, so that I can choose the right one for my use case.
---
@@ -35,11 +35,10 @@ The following tables summarize the available query connectors, tools, and integr
| Name | Functionality | Roles | Use cases |
| ---------------------------------------------------------------------------------------------------------- | ----------------------- | ------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------- |
| [Apache Spark](integrate-overview.md#apache-spark) | Query, Ingest, and Export | Data Analyst, Data Scientist | Machine learning (ML), Extract-Transform-Load (ETL), and Log Analytics scenarios using any Spark cluster |
-| [Apache Spark for Azure Synapse Analytics](integrate-overview.md#apache-spark-for-azure-synapse-analytics) | Query, Ingest, and Export | Data Analyst, Data Scientist | Machine learning (ML), Extract-Transform-Load (ETL), and Log Analytics scenarios using Synapse Analytics Spark cluster |
| [Azure Functions](integrate-overview.md#azure-functions) | Query, Ingest, and Orchestrate | Data Engineer, Application Developer | Integrate Azure Data Explorer into your serverless workflows to ingest data and run queries against your cluster |
| [JDBC](integrate-overview.md#jdbc) | Query | Application Developer | Use JDBC to connect to Azure Data Explorer databases and execute queries |
| [Logic Apps](integrate-overview.md#logic-apps) | Query and Orchestrate | Low Code Application Developer | Run queries and commands automatically as part of a scheduled or triggered task. |
-| [Matlab](integrate-overview.md#matlab) | Query | Data Analyst, Data Scientist | Analyse data, develop algorithms and create models. |
+| [Matlab](integrate-overview.md#matlab) | Query | Data Analyst, Data Scientist | Analye data, develop algorithms and create models. |
| [ODBC](integrate-overview.md#odbc) | Query | Application Developer | Establish a connection to Azure Data Explorer from any application that is equipped with support for the ODBC driver for SQL Serve. |
| [Power Apps](integrate-overview.md#power-apps) | Query and Orchestrate | Low Code Application Developer | Build a low code, highly functional app to make use of data stored in Azure Data Explorer |
| [Power Automate](integrate-overview.md#power-automate) | Query and Orchestrate | Low Code Application Developer | Orchestrate and schedule flows, send notifications, and alerts, as part of a scheduled or triggered task |
@@ -53,7 +52,7 @@ The following tables summarize the available query connectors, tools, and integr
| [Azure Monitor](/azure/data-explorer/integrate-overview?tabs=integrations#azure-monitor) | Query and Export | Data Engineer | Low cost data retention |
| [Jupyter Notebooks](/azure/data-explorer/integrate-overview?tabs=integrations#jupyter-notebooks) | Author Notebooks | Data Engineer, Data Scientist | Create and share documents containing live code, equations, visualizations for statistical modeling, data visualization, and machine learning using data stored in Azure Data Explorer. |
| [Kusto.Explorer](/azure/data-explorer/integrate-overview?tabs=integrations#kustoexplorer) | Query, Ingest, Admin and Dashboarding | Data Engineer, Data Analyst, Data Scientist | End-to-end data exploration |
-| [Kusto CLI](/azure/data-explorer/integrate-overview?tabs=integrations#kusto-cli) | Query and Admin | Aplication Admin, System Administrator | Send queries and control commands to an Azure Data Explorer cluster using command line utility |
+| [Kusto CLI](/azure/data-explorer/integrate-overview?tabs=integrations#kusto-cli) | Query and Admin | Application Admin, System Administrator | Send queries and control commands to an Azure Data Explorer cluster using command line utility |
| [Kusto Query Language parser](/azure/data-explorer/integrate-overview?tabs=integrations#kql-parser) | Query and Schema Exploration | Application Developer | Parse queries, perform semantic analysis, check for errors, and optimize your queries. |
| [Kusto Query Language Monaco editor](/azure/data-explorer/integrate-overview?tabs=integrations#monaco-editor-pluginembed) | Query, Admin, and Dashboarding | Application Developer, Data Engineer | Integrate Monaco Editor in your application |
| [Real-Time Intelligence in Microsoft Fabric](/azure/data-explorer/integrate-overview?tabs=integrations#real-time-analytics-in-microsoft-fabric) | Query, Ingest, Admin, and Dashboarding | Data Engineer, Data Analyst, Data Scientist | End-to-end data exploration |
diff --git a/data-explorer/media/stream-analytics-connector/stream-analytics-job-output.png b/data-explorer/media/stream-analytics-connector/stream-analytics-job-output.png
index a6d975c2f3..4247de2228 100644
Binary files a/data-explorer/media/stream-analytics-connector/stream-analytics-job-output.png and b/data-explorer/media/stream-analytics-connector/stream-analytics-job-output.png differ
diff --git a/data-explorer/media/stream-analytics-connector/stream-analytics-new-output.png b/data-explorer/media/stream-analytics-connector/stream-analytics-new-output.png
index a6d6ff6558..9f9f45dc47 100644
Binary files a/data-explorer/media/stream-analytics-connector/stream-analytics-new-output.png and b/data-explorer/media/stream-analytics-connector/stream-analytics-new-output.png differ
diff --git a/data-explorer/proof-of-concept-playbook.md b/data-explorer/proof-of-concept-playbook.md
index 24dedf3c5a..a83e694a26 100644
--- a/data-explorer/proof-of-concept-playbook.md
+++ b/data-explorer/proof-of-concept-playbook.md
@@ -2,8 +2,8 @@
title: "Azure Data Explorer POC playbook: Big data analytics"
description: "A high-level methodology for preparing and running an effective Azure Data Explorer proof of concept (POC) project."
ms.reviewer: devsha
-ms.topic: conceptual
-ms.date: 11/02/2023
+ms.topic: article
+ms.date: 12/01/2025
---
# Azure Data Explorer POC playbook: Big data analytics
@@ -29,7 +29,7 @@ The following scenarios are also good candidates for Azure Data Explorer:
- Low latency data store for real-time telemetry-based alerts
- [IoT telemetry data storage and analytics](/azure/architecture/solution-ideas/articles/iot-azure-data-explorer)
-- [High speed interactive analytics layer](/azure/architecture/solution-ideas/articles/interactive-azure-data-explorer). Particularly when used with Apache Spark engines such as Synapse Spark, DataBricks, or traditional data warehouses such as Synapse SQL pools.
+- [High speed interactive analytics layer](/azure/architecture/solution-ideas/articles/interactive-azure-data-explorer). Particularly when used with Apache Spark engines such as DataBricks.
- [Log and observability analytics](/azure/architecture/solution-ideas/articles/monitor-azure-data-explorer)
## Prepare for the POC
@@ -125,7 +125,7 @@ Here are the typical subject areas that are evaluated with Azure Data Explorer:
> Use the following frequently asked questions to help you plan your POC.
>
> - **How do I choose the SKU for my POC cluster?**
-> Use the [Select a SKU for your Azure Data Explorer cluster](manage-cluster-choose-sku.md) guide to help you choose the SKU for your POC cluster. When starting a POC, we recommend starting with a smaller SKUs and scale up SKU as required when you begin testing and capturing results.
+> Use the [Select a SKU for your Azure Data Explorer cluster](manage-cluster-choose-sku.md) guide to help you choose the SKU for your POC cluster. When starting a POC, we recommend starting with a smaller SKU and scale up the SKU as required when you begin testing and capturing results.
> - **How do I choose the caching period when creating my POC cluster?**
> To provide best query performance, ingested data is cached on the local SSD disk. This level of performance is not always required and less frequently queried data can often be stored on cheaper blob storage. Queries on data in blob storage run slower, but this acceptable in many scenarios. Knowing this can help you identify the number of compute nodes you need to hold your data in local SSD and continue to meet your query performance requirements. For example, if you you want to query *x* days worth of data (based on ingestion age) more frequently and retain data for *y* days and query it less frequently, in your cache retention policy, specify *x* as the value for hot cache retention and *y* as the value for the total retention. For more information, see [Cache policy](/kusto/management/cache-policy?view=azure-data-explorer&preserve-view=true).
> - **How do I choose the retention period when creating my POC cluster?**
diff --git a/data-explorer/security-network-restrict-outbound-access.md b/data-explorer/security-network-restrict-outbound-access.md
index 0038cac5c0..5de27f243b 100644
--- a/data-explorer/security-network-restrict-outbound-access.md
+++ b/data-explorer/security-network-restrict-outbound-access.md
@@ -3,7 +3,7 @@ title: Restrict outbound access from your Azure Data Explorer cluster
description: Learn how to restrict the outbound access from your Azure Data Explorer cluster to other services.
ms.reviewer: herauch
ms.topic: how-to
-ms.date: 04/10/2025
+ms.date: 12/01/2025
---
# Restrict outbound access from your Azure Data Explorer cluster
@@ -112,8 +112,7 @@ The following ARM template allows outbound access to specific FQDNs while keepin
"properties": {
"restrictOutboundNetworkAccess": "Enabled",
"allowedFqdnList": [
- "example.sql.azuresynapse.net",
- "example.blob.core.windows.net"
+ "example.blob.core.windows.net"
]
}
}
diff --git a/data-explorer/solution-architectures.md b/data-explorer/solution-architectures.md
index 01382c8845..e0e4471c59 100644
--- a/data-explorer/solution-architectures.md
+++ b/data-explorer/solution-architectures.md
@@ -3,7 +3,7 @@ title: Solution architectures in Azure
description: Learn about solution architectures in Azure Data Explorer.
ms.reviewer:
ms.topic: reference
-ms.date: 07/05/2022
+ms.date: 12/01/2025
---
# Solution architectures
@@ -15,7 +15,7 @@ This document refers you to all architectures that include Azure Data Explorer.
## Big data analytics with Azure Data Explorer
-Azure Data Explorer and Azure Synapse Analytics work together for near real-time analytics and modern data warehousing use cases.
+Azure Data Explorer works for near real-time analytics and modern data warehousing use cases.
> [!div class="nextstepaction"]
> [Big data analytics with Azure Data Explorer](/azure/architecture/solution-ideas/articles/big-data-azure-data-explorer)
@@ -82,7 +82,7 @@ This solution also uses the following services:
## Data analytics for automotive test fleets
-Automotive OEMs need solutions to minimize the time between doing test drives and getting test drive diagnostic data to R&D engineers.
+Automotive OEMs need solutions to minimize the time between doing test drives and getting test drive diagnostic data to R&D engineers.
This example workload relates to both telemetry and batch test drive data ingestion scenarios. The workload focuses on the data platform that processes diagnostic data, and the connectors for visualization and reporting.
diff --git a/data-explorer/spark-connector.md b/data-explorer/spark-connector.md
index abaed0f423..90131181d2 100644
--- a/data-explorer/spark-connector.md
+++ b/data-explorer/spark-connector.md
@@ -3,7 +3,7 @@ title: Use the Azure Data Explorer connector for Apache Spark to move data betwe
description: This topic shows you how to move data between Azure Data Explorer and Apache Spark clusters.
ms.reviewer: ohbitton
ms.topic: how-to
-ms.date: 11/03/2025
+ms.date: 12/01/2025
---
# Azure Data Explorer Connector for Apache Spark
@@ -14,9 +14,6 @@ The Kusto connector for Spark is an [open source project](https://github.com/Azu
You can write to Azure Data Explorer through queued ingestion or streaming ingestion. Reading from Azure Data Explorer supports column pruning and predicate pushdown, which filters the data in Azure Data Explorer, reducing the volume of transferred data.
-> [!NOTE]
-> For information about working with the Synapse Spark connector for Azure Data Explorer, see [Connect to Azure Data Explorer using Apache Spark for Azure Synapse Analytics](/azure/synapse-analytics/quickstart-connect-azure-data-explorer).
-
This article describes how to install and configure the Azure Data Explorer Spark connector and move data between Azure Data Explorer and Apache Spark clusters.
> [!NOTE]
diff --git a/data-explorer/stream-analytics-connector.md b/data-explorer/stream-analytics-connector.md
index 1251eb15ff..b3994248db 100644
--- a/data-explorer/stream-analytics-connector.md
+++ b/data-explorer/stream-analytics-connector.md
@@ -67,7 +67,7 @@ Before you begin, make sure you have an existing Stream Analytics job or [create
| Output alias | A friendly name used in queries to direct the query output to this database. |
| Subscription | Select the Azure subscription where your cluster resides. |
| Cluster | The unique name that identifies your cluster. The domain name [region].kusto.windows.net is appended to the cluster name you provide. The name can contain only lowercase letters and numbers. It must contain from 4 to 22 characters. |
- | Cluster URI | The data ingestion URI of your cluster. You can specify the URI for the Azure Data Explorer or [Azure Synapse Data Explorer](/azure/synapse-analytics/data-explorer/ingest-data/data-explorer-ingest-data-overview#programmatic-ingestion-using-sdks) data ingestion endpoints. |
+ | Cluster URI | The data ingestion URI of your Azure Data Explorer cluster. |
| Database | The name of the database where you're sending your output. The database name must be unique within the cluster. |
| Authentication | A [Microsoft Entra managed identity](/azure/active-directory/managed-identities-azure-resources/overview) that allows your cluster to easily access other Microsoft Entra protected resources. The identity is managed by the Azure platform and doesn't require you to provision or rotate any secrets. Managed identity configuration enables you to use customer-managed keys for your cluster. |
| Table | The name of the table where you're sending your output. The column names and data types in the Azure Stream Analytics output must match the schema of the Azure Data Explorer table. |
diff --git a/data-explorer/toc.yml b/data-explorer/toc.yml
index 2775766e72..9e23c9e841 100644
--- a/data-explorer/toc.yml
+++ b/data-explorer/toc.yml
@@ -636,8 +636,6 @@ items:
href: /sql/azure-data-studio/notebooks/notebooks-kqlmagic?context=/azure/data-explorer/context/context
- name: Azure Functions
href: integrate-azure-functions.md
- - name: Connect from Azure Synapse Apache Spark
- href: /azure/synapse-analytics/quickstart-connect-azure-data-explorer?toc=/azure/data-explorer/toc.json&bc=/azure/data-explorer/breadcrumb/toc.json
- name: Use MCP servers to build AI agents
href: integrate-mcp-servers.md
- name: Linked server