diff --git a/data-explorer/add-query-visualization.md b/data-explorer/add-query-visualization.md index a4c02ba96c..e8eef752f8 100644 --- a/data-explorer/add-query-visualization.md +++ b/data-explorer/add-query-visualization.md @@ -1,19 +1,19 @@ --- -title: Add a query visualization in the web UI - Azure Data Explorer +title: Add a Query Visualization in the Web UI - Azure Data Explorer description: Learn how to add a query visualization in the Azure Data Explorer web UI. ms.reviewer: mibar ms.topic: how-to -ms.date: 08/02/2023 +ms.date: 04/12/2026 --- # Add and modify a query visualization in the web UI -In this article, you'll learn how to create and customize visuals from query results, using the UI like that found in Azure Data Explorer Dashboards. These visuals can be further manipulated, and can be pinned in a [dashboard](azure-data-explorer-dashboards.md). The addition or modification of these visuals doesn't require rerunning the query, which can be especially useful for heavy queries. +In this article, you learn how to create and customize visuals from query results by using the UI, such as the one found in Azure Data Explorer Dashboards. You can further manipulate these visuals and pin them in a [dashboard](azure-data-explorer-dashboards.md). You don't need to rerun the query to add or modify these visuals. This feature is especially useful for heavy queries. For a full list of available visuals, see [Visualization](/kusto/query/render-operator?view=azure-data-explorer&preserve-view=true#visualization). For visuals that are only available in the web UI or dashboards, see [Dashboard-specific visuals](dashboard-visuals.md). ## Prerequisites -* A Microsoft account or a Microsoft Entra user identity. An Azure subscription isn't required. +* A Microsoft account or a Microsoft Entra user identity. You don't need an Azure subscription. * An Azure Data Explorer cluster and database. Use the publicly available [**help** cluster](https://dataexplorer.azure.com/help) or [create a cluster and database](create-cluster-and-database.md). ## Add a visual to a query @@ -43,26 +43,26 @@ For a full list of available visuals, see [Visualization](/kusto/query/render-op ## Change an existing visualization -There are two ways to use the visual formatting pane to change an existing visualization. +Use the visual formatting pane to change an existing visualization. ### Visual created with UI -If you've added a visual through the UI, you can change this visual by selecting the **Edit visual** tab in the results grid. +If you add a visual through the UI, you can change this visual by selecting the **Edit visual** tab in the results grid. :::image type="content" source="media/add-query-visualization/edit-visual.png" alt-text="Screenshot of edit visual tab in the results grid in Azure Data Explorer web UI."::: ### Visual created in query -If you've created a visual using the [render operator](/kusto/query/render-operator?view=azure-data-explorer&preserve-view=true), you can edit the visual by selecting **Visual** in the results grid. +If you create a visual by using the [render operator](/kusto/query/render-operator?view=azure-data-explorer&preserve-view=true), select **Visual** in the results grid to edit the visual. :::image type="content" source="media/add-query-visualization/change-rendered-visual.png" alt-text="Screenshot of rendered visual as a bar chart that has been changed to a column chart in the visual formatting pane in Azure Data Explorer web UI." lightbox="media/add-query-visualization/change-rendered-visual.png"::: > [!IMPORTANT] -> Notice that the visual formatting pane has changed the visual representation, but has not modified the original query. +> The visual formatting pane changes the visual representation, but doesn't modify the original query. ## Pin to dashboard -After you have formatted your visual, you can pin this visual to a new or existing dashboard. +After you format your visual, pin it to a new or existing dashboard. 1. From the visual formatting pane, select **Pin to dashboard**. diff --git a/data-explorer/create-event-hubs-connection.md b/data-explorer/create-event-hubs-connection.md index 161f6fecc4..5cbf1eeb4c 100644 --- a/data-explorer/create-event-hubs-connection.md +++ b/data-explorer/create-event-hubs-connection.md @@ -1,8 +1,8 @@ --- -title: 'Create an Event Hubs data connection - Azure Data Explorer' +title: Create an Event Hubs Data Connection - Azure Data Explorer description: Learn how to ingest data from Event Hubs into Azure Data Explorer. ms.topic: how-to -ms.date: 08/26/2025 +ms.date: 01/12/2026 ms.custom: - sfi-image-nochange --- @@ -39,7 +39,7 @@ In this section, you establish a connection between the event hub and your Azure 1. Right-click on the database where you want to ingest the data. Select **Get data**. - :::image type="content" source="media/get-data-event-hubs/get-data.png" alt-text="Screenshot of query tab, with right-click on a database and the get options dialog open." lightbox="media/get-data-event-hubs/get-data.png"::: + :::image type="content" source="media/get-data-file/get-data.png" alt-text="Screenshot of query tab, with right-click on a database and the get options dialog open." lightbox="media/get-data-file/get-data.png"::: ### Source @@ -47,7 +47,7 @@ In the **Get data** window, the **Source** tab is selected. Select the data source from the available list. In this example, you're ingesting data from **Event Hubs**. -:::image type="content" source="media/get-data-file/select-data-source.png" alt-text="Screenshot of get data window with source tab selected." lightbox="media/get-data-file/select-data-source.png"::: +:::image type="content" source="media/get-data-event-hubs/select-data-source.png" alt-text="Screenshot of get data window with source tab selected." lightbox="media/get-data-event-hubs/select-data-source.png"::: ### Configure diff --git a/data-explorer/data-lake-query-data.md b/data-explorer/data-lake-query-data.md index 836b56a687..0512ac1a26 100644 --- a/data-explorer/data-lake-query-data.md +++ b/data-explorer/data-lake-query-data.md @@ -1,10 +1,11 @@ --- -title: Query data in Azure Data Lake using Azure Data Explorer +title: Query Data in Azure Data Lake Using Azure Data Explorer description: Learn how to query data in Azure Data Lake using Azure Data Explorer. ms.reviewer: orspodek ms.topic: how-to -ms.date: 06/10/2025 +ms.date: 04/12/2026 --- + # Query data in Azure Data Lake using Azure Data Explorer Azure Data Lake Storage is a highly scalable and cost-effective data lake solution for big data analytics. It combines the power of a high-performance file system with massive scale and economy to help you reduce your time to insight. Data Lake Storage Gen2 extends Azure Blob Storage capabilities and is optimized for analytics workloads. @@ -49,7 +50,7 @@ dataformat=csv The external table is now visible in the left pane of the Azure Data Explorer web UI: -:::image type="content" source="media/data-lake-query-data/external-tables-web-ui.png" alt-text="Screenshot that shows external table in Azure Data Explorer web UI."::: +:::image type="content" source="media/data-lake-query-data/external-tables.png" alt-text="Screenshot that shows external table in Azure Data Explorer web UI."::: ## External table permissions @@ -200,9 +201,7 @@ dataformat=csv ) ``` -You can find the created **TaxiRides** table by looking at the left pane of the Azure Data Explorer web UI: - -:::image type="content" source="media/data-lake-query-data/taxirides-external-table.png" alt-text=" Screenshot showing the Taxi rides external table."::: +You can find the created **TaxiRides** table by looking at the left pane of the Azure Data Explorer web UI. ### Query *TaxiRides* external table data diff --git a/data-explorer/get-data-amazon-s3.md b/data-explorer/get-data-amazon-s3.md index fe304910bf..2b0f39960a 100644 --- a/data-explorer/get-data-amazon-s3.md +++ b/data-explorer/get-data-amazon-s3.md @@ -1,48 +1,48 @@ --- -title: Get data from Amazon S3 into Azure Data Explorer +title: Get Data from Amazon S3 Into Azure Data Explorer description: Learn how to get data from Amazon S3 into Azure Data Explorer. ms.reviewer: sharmaanshul ms.topic: how-to -ms.date: 11/16/2023 +ms.date: 04/12/2026 ms.custom: sfi-image-nochange --- # Get data from Amazon S3 -Data ingestion is the process used to load data from one or more sources into a table in Azure Data Explorer. Once ingested, the data becomes available for query. In this article, you learn how to get data from Amazon S3 into either a new or existing table. +Data ingestion is the process of loading data from one or more sources into a table in Azure Data Explorer. After ingestion, the data is available for query. In this article, you learn how to get data from Amazon S3 into either a new or existing table. -For more information on Amazon S3, see [What is Amazon S3?](https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html). +For more information on Amazon S3, see [What is Amazon S3?](https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html) For general information on data ingestion, see [Azure Data Explorer data ingestion overview](ingest-data-overview.md). ## Prerequisites -* A Microsoft account or a Microsoft Entra user identity. An Azure subscription isn't required. -* Sign-in to the [Azure Data Explorer web UI](https://dataexplorer.azure.com/home). +* A Microsoft account or a Microsoft Entra user identity. You don't need an Azure subscription. +* Sign in to the [Azure Data Explorer web UI](https://dataexplorer.azure.com/home). * An Azure Data Explorer cluster and database. [Create a cluster and database](create-cluster-and-database.md). ## Get data -1. From the left menu, select **Query**. -1. Right-click on the database where you want to ingest the data, and then select **Get data**. +1. Select **Query** from the left menu. +1. Right-click the database where you want to ingest the data, and then select **Get data**. - :::image type="content" source="media/get-data-amazon-s3/get-data.png" alt-text="Screenshot of query tab, with right-click on a database and the get options dialog open." lightbox="media/get-data-amazon-s3/get-data.png"::: + :::image type="content" source="media/get-data-file/get-data.png" alt-text="Screenshot of query tab, with right-click on a database and the get options dialog open." lightbox="media/get-data-file/get-data.png"::: ## Source In the **Get data window**, the **Source** tab is selected. -Select the data source from the available list. In this example, you are ingesting data from **Amazon S3**. +Select the data source from the available list. In this example, ingest data from **Amazon S3**. -:::image type="content" source="media/get-data-amazon-s3/select-data-source.png" alt-text="Screenshot of get data window with source tab selected." lightbox="media/get-data-amazon-s3/select-data-source.png"::: +:::image type="content" source="media/get-data-file/source.png" alt-text="Screenshot of get data window with source tab selected." lightbox="media/get-data-file/source.png"::: ## Configure -1. Select a target database and table. If you want to ingest data into a new table, select **+New table** and enter a table name. +1. Select a target database and table. To ingest data into a new table, select **+ New table** and enter a table name. > [!NOTE] - > Table names can be up to 1024 characters including spaces, alphanumeric, hyphens, and underscores. Special characters aren't supported. + > Table names can be up to 1,024 characters, including spaces, alphanumeric characters, hyphens, and underscores. Special characters aren't supported. -1. In the **URI** field, paste the connection string of a single bucket, or an individual object in the following format. +1. In the **URI** field, paste the connection string for a single bucket or an individual object in the following format. > Bucket: `https://`*BucketName*`.s3.`*RegionName*`.amazonaws.com` > @@ -82,7 +82,7 @@ Optionally: ## Summary -In the **Data preparation** window, all three steps are marked with green check marks when data ingestion finishes successfully. You can view the commands that were used for each step, or select a card to query, visualize, or drop the ingested data. +In the **Data preparation** window, all three steps show green check marks when data ingestion finishes successfully. You can view the commands that each step uses, or select a card to query, visualize, or drop the ingested data. :::image type="content" source="media/get-data-amazon-s3/summary.png" alt-text="Screenshot of summary page with successful ingestion completed." lightbox="media/get-data-amazon-s3/summary.png"::: diff --git a/data-explorer/ingest-data-historical.md b/data-explorer/ingest-data-historical.md index 4d1a55097f..df615ddcde 100644 --- a/data-explorer/ingest-data-historical.md +++ b/data-explorer/ingest-data-historical.md @@ -1,14 +1,15 @@ --- -title: Ingest historical data into Azure Data Explorer +title: Ingest Historical Data Into Azure Data Explorer description: Learn how to use LightIngest to ingest historical or ad hoc data ingestion into Azure Data Explorer. ms.reviewer: vplauzon ms.topic: how-to -ms.date: 11/03/2025 +ms.date: 04/12/2026 # CustomerIntent: As a data analyst, I want to learn how to ingest historical data into Azure Data Explorer, so that I can analyze it and gain insights. --- + # How to ingest historical data into Azure Data Explorer -A common scenario when onboarding to Azure Data Explorer is to ingest historical data, sometimes called backfill. The process involves ingesting data from an existing storage system into a table, which is a collection of [extents](/kusto/management/extents-overview?view=azure-data-explorer&preserve-view=true). +A common scenario when onboarding to Azure Data Explorer is ingesting historical data, sometimes called backfill. The process involves ingesting data from an existing storage system into a table, which is a collection of [extents](/kusto/management/extents-overview?view=azure-data-explorer&preserve-view=true). Ingest historical data by using the [creationTime ingestion property](/kusto/ingestion-properties?view=azure-data-explorer&preserve-view=true#ingestion-properties) to set the creation time of extents to the time the data was *created*. Using the creation time as the ingestion partitioning criterion can age your data in accordance with your [cache](/kusto/management/cache-policy?view=azure-data-explorer&preserve-view=true) and [retention](/kusto/management/retention-policy?view=azure-data-explorer&preserve-view=true) policies, and make time filters more efficient. @@ -16,20 +17,20 @@ By default, the creation time for extents is set to the time when you ingest the - All the data lands in cache and stays there for 30 days, using more cache than you anticipated. - Older data isn't removed one day at a time; hence data is retained in the cluster for longer than necessary and, after two years, is all removed at once. -- Data, previously grouped by date in the source system, may now be [batched together](/kusto/management/batching-policy?view=azure-data-explorer&preserve-view=true) in the same extent leading to inefficient queries. +- Data, previously grouped by date in the source system, might now be [batched together](/kusto/management/batching-policy?view=azure-data-explorer&preserve-view=true) in the same extent leading to inefficient queries. :::image type="content" source="media/ingest-data-historical/historical-data-expected-vs-actual.png" alt-text="Diagram showing the expected versus actual result of ingesting historical data using the default creation time."::: In this article, you learn how to partition historical data: -- Using the `creationTime` ingestion property during ingestion (recommended) +- Use the `creationTime` ingestion property during ingestion (recommended) - Where possible, ingest historical data by using the [`creationTime` ingestion property](/kusto/ingestion-properties?view=azure-data-explorer&preserve-view=true#ingestion-properties), which allows you to set the creation time of the extents by extracting it from the file or blob path. If your folder structure doesn't use a creation date pattern, restructure your file or blob path to reflect the creation time. By using this method, the data is ingested into the table with the correct creation time, and the cache and retention periods are applied correctly. + Where possible, ingest historical data by using the [`creationTime` ingestion property](/kusto/ingestion-properties?view=azure-data-explorer&preserve-view=true#ingestion-properties), which you can use to set the creation time of the extents by extracting it from the file or blob path. If your folder structure doesn't use a creation date pattern, restructure your file or blob path to reflect the creation time. By using this method, you ingest the data into the table with the correct creation time, and the cache and retention periods are applied correctly. > [!NOTE] > By default, extents are partitioned by time of creation (ingestion), and in most cases there's no need to set a data partitioning policy. -- Using a partitioning policy post ingestion +- Use a partitioning policy post ingestion If you can't use the `creationTime` ingestion property, for example if you're [ingesting data using the Azure Cosmos DB connector](ingest-data-cosmos-db-connection.md) where you can't control the creation time or if you can't restructure your folder structure, you can repartition the table post ingestion to achieve the same effect by using the [partitioning policy](/kusto/management/partitioning-policy?view=azure-data-explorer&preserve-view=true). However, this method might require some trial and error to optimize policy properties and is less efficient than using the `creationTime` ingestion property. Use this method only when using the `creationTime` ingestion property isn't possible. @@ -50,18 +51,18 @@ LightIngest is useful to load historical data from an existing storage system to ### Destination -1. In the Azure Data Explorer web UI, from the left menu, select **Query**. +1. In the Azure Data Explorer web UI, select **Query** from the left menu. -1. Right-click the database where you want to ingest the data, then select **LightIngest**. +1. Right-click the database where you want to ingest the data, and then select **LightIngest**. :::image type="content" source="media/ingest-data-historical/ingest-data-from-query-page.png" alt-text="Screenshot of the Azure Data Explorer web UI showing the database more menu." lightbox="media/ingest-data-historical/ingest-data-from-query-page.png"::: The **Ingest data** window opens with the **Destination** tab selected. The **Cluster** and **Database** fields are automatically populated. -1. Select a target table. To ingest data into a new table, select **New table**, then enter a table name. +1. Select a target table. To ingest data into a new table, select **New table**, and then enter a table name. > [!NOTE] - > Table names can be up to 1,024 characters including spaces, alphanumeric characters, hyphens, and underscores. Special characters aren't supported. + > Table names can be up to 1,024 characters, including spaces, alphanumeric characters, hyphens, and underscores. Special characters aren't supported. :::image type="content" source="media/ingest-data-historical/ingest-new-data.png" alt-text="Screenshot of the destination tab showing the destination database and table."::: @@ -70,14 +71,14 @@ LightIngest is useful to load historical data from an existing storage system to ### Source 1. Under **Select source**, select either **Add URL** or **Select container**. - - When adding a URL, under **Link to source**, specify the account key or SAS URL to a container. You can create the SAS URL [manually](/azure/vs-azure-tools-storage-explorer-blobs#get-the-sas-for-a-blob-container) or [automatically](/kusto/api/connection-strings/generate-sas-token?view=azure-data-explorer&preserve-view=true). - - When selecting a container from your storage account, select your **Storage subscription**, **Storage account**, and **Container** from the dropdown menus. + - When you add a URL, under **Link to source**, specify the account key or SAS URL to a container. You can create the SAS URL [manually](/azure/vs-azure-tools-storage-explorer-blobs#get-the-sas-for-a-blob-container) or [automatically](/kusto/api/connection-strings/generate-sas-token?view=azure-data-explorer&preserve-view=true). + - When you select a container from your storage account, select your **Storage subscription**, **Storage account**, and **Container** from the dropdown menus. :::image type="content" source="media/ingest-data-historical/source-tab-container-from-subscription.png" alt-text="Screenshot of dialog box for selecting container from storage subscription and account."::: [!INCLUDE [ingestion-size-limit](includes/cross-repo/ingestion-size-limit.md)] -1. Select **Advanced settings** to define additional settings for the ingestion process using LightIngest. +1. Select **Advanced settings** to define additional settings for the ingestion process by using LightIngest. :::image type="content" source="media/ingest-data-historical/source-tab-advanced-settings.png" alt-text="Screenshot of selecting advanced settings for the ingestion processing involving the tool LightIngest."::: @@ -88,7 +89,7 @@ LightIngest is useful to load historical data from an existing storage system to | Property | Description| |---|---| | **Creation time pattern** | Specify to override the ingestion time property of the created extent with a pattern, for example, to apply a date based on the folder structure of the container. See also [Creation time pattern](lightingest.md#ingest-historical-data-with-the-creationtime-property). | - | **Blob name pattern** | Specify the pattern used to identify the files to ingest. Ingest all the files that match the blob name pattern in the given container. Supports wildcards. We recommended enclosing in double quotes. | + | **Blob name pattern** | Specify the pattern used to identify the files to ingest. Ingest all the files that match the blob name pattern in the given container. Supports wildcards. Enclose the pattern in double quotes. | | **Tag** | A [tag](/kusto/management/extent-tags?view=azure-data-explorer&preserve-view=true) assigned to the ingested data. The tag can be any string. | | **Limit amount of files** | Specify the number of files to ingest. Ingests the first `n` files that match the blob name pattern, up to the number specified. | | **Don't wait for ingestion to complete** | If set, queues the blobs for ingestion without monitoring the ingestion process. If not set, LightIngest continues to poll the ingestion status until ingestion is complete.| @@ -111,7 +112,7 @@ The schema tab provides a preview of the data. To generate the LightIngest command, select **Next: Start Ingestion**. -Optionally: +Optionally, you can: - Change the automatically inferred **Data format** by selecting the desired format from the dropdown menu. - Change the automatically inferred **Mapping name**. You can use alphanumeric characters and underscores. Spaces, special characters, and hyphens aren't supported. @@ -136,13 +137,13 @@ Optionally: #### Step 1: Prepare for repartitioning -1. Adjust the retention policy to keep old data. In the following example, you set the retention policy for table **MyTable** to 10 years. +1. Adjust the retention policy to keep old data. In the following example, set the retention policy for table **MyTable** to 10 years. ```kusto .alter-merge table MyTable policy retention softdelete = 3650d recoverability = enabled ``` -1. Adjust the caching policy so that all the data is in hot cache for the repartitioning, as only hot data can be repartitioned post ingestion. In the following example, you set the caching for table **MyTable** to 10 years. +1. Adjust the caching policy so that all the data is in hot cache for the repartitioning, as only hot data can be repartitioned post ingestion. In the following example, set the caching for table **MyTable** to 10 years. ```kusto .alter table MyTable policy caching hot = 3650d @@ -153,7 +154,7 @@ Optionally: #### Step 2: Initiate repartitioning -1. Create a partitioning policy that partitions the data by the column named `Timestamp`. In the following example, you set the partitioning policy for table **MyTable** to partition by the column named `Timestamp`. +1. Create a partitioning policy that partitions the data by the column named `Timestamp`. In the following example, set the partitioning policy for table **MyTable** to partition by the column named `Timestamp`. ~~~kusto .alter table MyTable policy partitioning @@ -178,10 +179,10 @@ Optionally: For information about the partitioning policy properties, see [partition properties](/kusto/management/partitioning-policy?view=azure-data-explorer&preserve-view=true#partition-properties-1). For historical ingestion, how you set the following properties is important: - Set the **EffectiveDateTime** property to a date earlier than the start of the ingestion to trigger the repartitioning. - - Set the **RangeSize** to one day so that the data is repartitioned into buckets of one day. However, you should set this value to align with your data. For example, if you have less than several GBs of data per day, consider setting a larger value. + - Set the **RangeSize** to one day so that the data is repartitioned into buckets of one day. However, set this value to align with your data. For example, if you have less than several GBs of data per day, consider setting a larger value. - Set the **OverrideCreationTime** to *true* so that after repartitioning the data into day buckets, the extents are marked with that day as the creation time. -1. Set a merge policy to allow merging of all extents, including extents older than 14 days. Setting this policy is important because the repartitioning process creates extents older than 14 days, which by default are excluded by the merge process. +1. Set a merge policy to allow merging of all extents, including extents older than 14 days. Set this policy because the repartitioning process creates extents older than 14 days, which by default the merge process excludes. ~~~kusto .alter table MyTable policy merge @@ -198,7 +199,7 @@ Optionally: #### Step 3: Clean up post repartitioning -Once the repartitioning is complete, you can clean up the policies you set in the previous steps. +When the repartitioning is complete, clean up the policies you set in the previous steps. 1. 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Go to the settings -Select the settings icon :::image type="icon" source="media/web-customize-settings/settings-icon.png" border="false"::: on the top right, to open the **Settings** window. +Select the settings icon on the top right, to open the **Settings** window. -:::image type="content" source="media/web-customize-settings/settings-pane.png" alt-text="Screenshot of the Settings window."::: +:::image type="content" source="media/web-customize-settings/settings-icon.png" alt-text="Screenshot of the Settings window and the settings icon highlighted."::: ## Set query recommendations diff --git a/data-explorer/web-results-grid.md b/data-explorer/web-results-grid.md index 72b53d0b02..f49b30e32f 100644 --- a/data-explorer/web-results-grid.md +++ b/data-explorer/web-results-grid.md @@ -1,30 +1,25 @@ --- -title: 'Azure Data Explorer web UI results grid' +title: Azure Data Explorer Web UI Results Grid description: Learn how to work with the results grid in the Azure Data Explorer web UI. ms.topic: how-to -ms.date: 05/28/2023 +ms.date: 04/12/2026 ms.custom: sfi-image-nochange --- # Azure Data Explorer web UI results grid -In this guide, you'll learn how to work with query results in the [Azure Data Explorer web UI](https://dataexplorer.azure.com/home) using the results grid. With the results grid, you can customize and manipulate your results, and enhance the efficiency and effectiveness of your data analysis. +In this guide, you learn how to work with query results in the [Azure Data Explorer web UI](https://dataexplorer.azure.com/home) by using the results grid. By using the results grid, you can customize and manipulate your results, and enhance the efficiency and effectiveness of your data analysis. To learn how to run queries, see [Quickstart: Query data in the Azure Data Explorer web UI](web-query-data.md). ## Prerequisites -* A Microsoft account or a Microsoft Entra user identity. An Azure subscription isn't required. +* A Microsoft account or a Microsoft Entra user identity. You don't need an Azure subscription. * An Azure Data Explorer cluster and database. Use the publicly available [**help** cluster](https://dataexplorer.azure.com/help) or [create a cluster and database](create-cluster-and-database.md). ## Expand a cell -Expand a cell to open a detailed view of the cell content, which is especially helpful for viewing [dynamic](/kusto/query/scalar-data-types/dynamic?view=azure-data-explorer&preserve-view=true) data or long strings. In the detailed view, dynamic data is presented like JSON. To expand a cell, follow these steps: - -1. Double-click a cell to open the detailed view. -1. Select the icon on the top right of the result grid to switch reading pane modes. Choose between the following reading pane modes: **Inline**, **Below**, and **Right**. - - :::image type="content" source="media/web-query-data/expanded-view-icon.png" alt-text="Screenshot showing the icon to change the reading pane mode in the Azure Data Explorer web UI query results." lightbox="media/web-query-data/expanded-view-icon.png"::: +Expand a cell to copy or view the cell content. This feature is especially helpful for viewing [dynamic](/kusto/query/scalar-data-types/dynamic?view=azure-data-explorer&preserve-view=true) data or long strings. Left-click a cell to open the detailed view. ## Expand a row @@ -32,12 +27,19 @@ Expand a row to open a detailed view of the row content. This detailed view show 1. On the left side of the row you want to expand, select the arrow icon **>**. - :::image type="content" source="media/web-query-data/expand-row-arrow.png" alt-text="Screenshot of an expanded row in the Azure Data Explorer web UI." lightbox="media/web-query-data/expand-row-arrow.png"::: + :::image type="content" source="media/web-query-data/expand-row-arrow.png" alt-text="Screenshot of the arrow that expands the row in the Azure Data Explorer web UI." lightbox="media/web-query-data/expand-row-arrow.png"::: + +1. In the detailed view that opens, you can do the following tasks: -1. In the detailed view, columns with dynamic data can be expanded or collapsed. Expanded columns are marked by a downward-pointing arrow, while collapsed columns are marked by a right-pointing arrow. You can toggle between expanding and collapsing the content by selecting the arrow beside the column key. + :::image type="content" source="media/web-query-data/expand-row.png" alt-text="Screenshot of an expanded row in the Azure Data Explorer web UI." lightbox="media/web-query-data/expand-row.png"::: + + - View and copy the content of each column. + - Search within the detailed view. To learn how to do so, see [Search in detailed view](#search-in-detailed-view). + - Switch reading pane modes between **Inline**, **Below**, and **Right**. The default mode is **Inline**, which shows the detailed view in a flyout pane. The **Below** and **Right** modes show the detailed view in a docked pane below or to the right of the results grid, respectively. :::image type="content" source="media/web-query-data/expand-columns.png" alt-text="Screenshot of columns with expanded or collapsed data." lightbox="media/web-query-data/expand-columns.png"::: + ## Search in detailed view You can perform free text search within the detailed view of a result. To learn how to do so, follow these steps: @@ -79,7 +81,7 @@ Nested dynamic property-bag fields can become complex as you go deeper into thei | take 10 ``` -1. Select the first result in the `StormSummary` column, which should be the last column. +1. Select the first result in the `StormSummary` column, which is the last column. 1. Select different fields within the result and see how the JPATH at the top of the window changes. For example, the following screenshot shows the path to the `Location` field, which is nested under the `Details` field within the `StormSummary` column dynamic property-bag object. @@ -89,7 +91,7 @@ Nested dynamic property-bag fields can become complex as you go deeper into thei ## Add filter from dynamic field -To add a specific dynamic field as a filter to your query, do the following: +To add a specific dynamic field as a filter to your query, complete the following steps: 1. Run the following query. @@ -101,9 +103,9 @@ To add a specific dynamic field as a filter to your query, do the following: | take 10 ``` -1. Select the first result in the `StormSummary` column, which should be the last column. +1. Select the first result in the `StormSummary` column, which is the last column. -1. Right-click on a field within a dynamic data and select **Add as filter**. For example, right-click on the `Location` field and add it as a filter. +1. Right-click a field within a dynamic data and select **Add as filter**. For example, right-click the `Location` field and add it as a filter. :::image type="content" source="media/web-query-data/add-dynamic-field-as-filter.png" alt-text="Screenshot of add as filter option from dynamic field." lightbox="media/web-query-data/add-dynamic-field-as-filter.png"::: @@ -149,7 +151,7 @@ Within a result set, you can group the results by any column. After this groupin | where EventType == "Lake-Effect Snow" ``` -1. Mouse-over the **State** column, select the menu, and select **Group by State**. +1. Mouse over the **State** column, select the menu, and select **Group by State**. :::image type="content" source="media/web-query-data/group-by.png" alt-text="Screenshot of a table with query results grouped by state." lightbox="media/web-query-data/group-by.png"::: @@ -161,7 +163,7 @@ Within a result set, you can group the results by any column. After this groupin :::image type="content" source="media/web-query-data/group-expanded.png" alt-text="Screenshot of a query results grid with California group expanded in the Azure Data Explorer web U I." border="false" lightbox="media/web-query-data/group-expanded.png"::: -1. Once you've grouped data by a column, you can use a value aggregation function to calculate statistics for each group. To do so, go to the column menu, choose **Value Aggregation**, and select the function type to use for that column. +1. Once you group data by a column, use a value aggregation function to calculate statistics for each group. Go to the column menu, choose **Value Aggregation**, and select the function type to use for that column. :::image type="content" source="media/web-query-data/aggregate.png" alt-text="Screenshot of aggregate results when grouping column by results in the Azure Data Explorer web UI." lightbox="media/web-query-data/aggregate.png"::: @@ -234,7 +236,7 @@ To search for a specific expression within a result table, use the search capabi :::image type="content" source="media/web-query-data/search.png" alt-text="Screenshot highlighting the search bar in the table." lightbox="media/web-query-data/search.png"::: -1. All mentions of your searched expression are now highlighted in the table. You can navigate between them by clicking Enter to go forward, Shift+Enter to go backward, or by using the up and down buttons beside the search box to move around. +1. All mentions of your searched expression are now highlighted in the table. You can navigate between them by pressing Enter to go forward, Shift+Enter to go backward, or by using the up and down buttons beside the search box to move around. :::image type="content" source="media/web-query-data/search-results.png" alt-text="Screenshot of a table containing highlighted expressions from search results." lightbox="media/web-query-data/search-results.png":::