Merge pull request #3122 from MicrosoftDocs/main639185006507438961sync_temp

For protected branch, push strategy should use PR and merge to target branch method to work around git push error

Author: learn-build-service-prod[bot]

docs/data-engineering/materialized-lake-views/materialized-lake-views-public-api.md

@@ -4,15 +4,15 @@ description: In this fifth part of the tutorial series, learn how to get set up
 ms.author: lagayhar
 ms.reviewer: ruxu
 ms.topic: tutorial
-ms.date: 04/25/2025
+ms.date: 06/30/2026
 #CustomerIntent: As a data scientist, I want to create a Power BI report to visualize the predictions data.
 ---
 
 # Tutorial Part 5: Visualize predictions with a Power BI report
 
 In this tutorial, you build a Power BI report from the predictions data you generated in [Part 4: Perform batch scoring and save predictions to a lakehouse](tutorial-data-science-batch-scoring.md).
 
-You'll learn how to:
+You learn how to:
 
 > [!div class="checklist"]
 >
@@ -25,7 +25,7 @@ You'll learn how to:
 
 [!INCLUDE [prerequisites](./includes/prerequisites.md)]
 
-This is part 5 of 5 in the tutorial series. To complete this tutorial, first complete:
+This tutorial is part 5 of 5 in the tutorial series. To complete this tutorial, first complete:
 
 * [Part 1: Ingest data into a Microsoft Fabric lakehouse using Apache Spark](tutorial-data-science-ingest-data.md).
 * [Part 2: Explore and visualize data using Microsoft Fabric notebooks](tutorial-data-science-explore-notebook.md) to learn more about the data.
@@ -128,7 +128,7 @@ Add some measures to the semantic model:
 
 ## Create a new report
 
-Once you complete all of the operations described earlier, select **Create new report** in the top ribbon File option list to open the Power BI report authoring page, as shown in the following screenshot:
+After you complete all of the operations described earlier, select **Create new report** in the top ribbon File option list to open the Power BI report authoring page, as shown in the following screenshot:
 
 :::image type="content" source="media/tutorial-data-science-create-report/visualize-this-data.png" alt-text="Screenshot shows how to create a report.":::
 
@@ -212,7 +212,7 @@ The Power BI report shows that:
 
 ## Next step
 
-This completes the five part tutorial series. See other end-to-end sample tutorials:
+This tutorial series is complete. For other end-to-end sample tutorials, see:
 
 > [!div class="nextstepaction"]
 > [How to use end-to-end AI samples in Microsoft Fabric](use-ai-samples.md)

docs/data-engineering/materialized-lake-views/media/materialized-lake-views-public-api/materialized-lake-views-refresh-all.png

@@ -0,0 +1,92 @@
+---
+title: Time series analysis in Real-Time Dashboard
+description: Learn how to use the Time Series visualization and KQL capabilities for time series analysis in Real-Time Dashboards in Microsoft Fabric.
+ms.reviewer: mibar
+ms.topic: concept-article
+ms.subservice: rti-dashboard
+ms.date: 06/29/2026
+ai-usage: ai-assisted
+---
+
+# Time series analysis in Real-Time Dashboard (preview)
+
+Time series analysis helps you understand how data behaves over time. Whether you're tracking sensor readings, monitoring system performance, or analyzing business trends, visualizing and exploring time-based data leads to better decisions. Real-Time Dashboards in Microsoft Fabric provide tools for both visual exploration and code-based analysis of Time series data.
+
+This article introduces the Time Series visualization for Real-Time Dashboards and provides an overview of Kusto Query Language (KQL) capabilities for advanced Time series analysis.
+
+[!INCLUDE [feature-preview-note](../includes/feature-preview-note.md)]
+
+## When to use Time series analysis
+
+Time series analysis is useful when data changes over time and patterns matter. Common use cases include:
+
+- **IoT and equipment monitoring**: Compare sensor readings against historical patterns to identify equipment drifting out of tolerance or approaching failure.
+- **Operational analytics**: Track application response times, error rates, or throughput over time. Navigate through service tiers or geographic regions to find performance bottlenecks.
+- **Business trend analysis**: Analyze sales, revenue, or customer engagement patterns across time periods. Compare this quarter to last quarter, or this year to the same period last year, with aligned timeframes that make seasonal patterns visible.
+- **Anomaly detection**: Identify unexpected spikes, dips, or deviations from normal baseline patterns in your data.
+
+## Time series visualization
+
+The Time series visual in Real-Time Dashboards provides dedicated capabilities for navigating, comparing, and customizing Time series data. Unlike traditional charts, this visual is designed for the unique characteristics of time-based data: trends that emerge over periods, seasonal patterns, and anomalies.
+
+:::image type="content" source="media/dashboard-time-series/time-series-chart.png" alt-text="Screenshot of a Time series visual." lightbox="media/dashboard-time-series/time-series-chart.png":::
+
+### Navigate multivariate data
+
+Time series datasets often contain dozens or even hundreds of metrics. A single dashboard tile tracking machine performance across a factory floor might display readings from every sensor on every piece of equipment. The Time series visual provides tools to find the data that matters:
+
+- **Legend search**: Use the legend search bar to locate specific data series by name.
+- **Series selection**: Select a series from the chart or legend, and corresponding elements highlight automatically.
+- **Entities panel**: View your data series in a hierarchical tree structure based on your entity selections. Filter the list to find specific series, expand or collapse groups, and use checkboxes to show or hide individual series in the chart.
+- **Navigation tree**: For datasets with logical groupings, such as sensors organized by machine or metrics organized by region, navigate through groups using the navigation tree on the right.
+
+### Adjust the timeline
+
+The Time series visual renders multiple measures as separate charts, each with its own time axis and synchronized time slider. As you adjust the time range, all charts update together, keeping your analysis aligned across different metrics.
+
+Use the time slider to:
+
+- Drag to select a specific timeframe.
+- Enter exact values to render all charts in a specific timeframe.
+- Zoom in on areas of interest while maintaining context.
+
+:::image type="content" source="media/dashboard-time-series/adjust-timeline.png" alt-text="Screenshot of the Time series visual with the time slider highlighted." lightbox="media/dashboard-time-series/adjust-timeline.png":::
+
+### Customize your visual
+
+The Time series visual editor gives you control over how you organize and display your data. In the **Data** section, define your Time series structure by selecting:
+
+- **Measured entities**: The dimensions that categorize your data, such as region, machine, or event type.
+- **Measured data**: The numeric values you want to track over time.
+
+Additional customization options include:
+
+| Option | Description |
+|--------|-------------|
+| Y-axis scaling | Choose between global, separate, or adaptive scaling with outlier removal. |
+| Color assignments | Assign colors via color picker or palette. |
+| Zoom controls | Zoom in and out on the data. |
+| Axis scales | Switch between linear and logarithmic axis scales. |
+
+For more information on customizing Time series visuals, see [Customize Real-Time Dashboard visuals](dashboard-visuals-customize.md#time-series-visual-preview).
+
+## Advanced Time series analysis with KQL
+
+For deeper analysis beyond visual exploration, KQL provides native support for creating, processing, and analyzing Time series data. By using KQL, you can:
+
+- **Create Time series**: Use the `make-series` operator to aggregate data into regular time bins, fill in missing values, and partition by dimensions.
+- **Filter and smooth data**: Apply moving averages and other filtering techniques to reduce noise and highlight trends.
+- **Detect trends and seasonality**: Identify linear trends, trend changes, and periodic patterns in your data.
+- **Detect anomalies**: Find outliers and unexpected deviations from baseline patterns.
+- **Forecast future values**: Predict upcoming values by extrapolating seasonal and trend components.
+
+For more information about KQL Time series capabilities, see:
+
+- [Time series analysis](/kusto/query/time-series-analysis?view=microsoft-fabric&preserve-view=true)
+- [Anomaly detection and forecasting](/kusto/query/anomaly-detection?view=microsoft-fabric&preserve-view=true)
+
+## Related content
+
+- [Customize Real-Time Dashboard visuals](dashboard-visuals-customize.md)
+- [Real-Time Dashboard-specific visuals](dashboard-visuals.md)
+- [Create a Real-Time Dashboard](dashboard-real-time-create.md)

docs/data-engineering/materialized-lake-views/media/materialized-lake-views-public-api/materialized-lake-views-refresh-specific.png

@@ -3,7 +3,7 @@ title: Configure Settings for a Fabric Eventstream
 description: This article describes how to configure sensitivity label, endorsement, retention, and throughput settings for an eventstream. 
 ms.reviewer: xujiang1
 ms.topic: how-to
-ms.date: 03/22/2026
+ms.date: 05/09/2026
 ms.search.form: Eventstreams Overview
 ---
 
@@ -41,9 +41,9 @@ Here's how different sources and destinations perform at each throughput level.
 > [!NOTE]
 > The throughput upper limits listed here are based on the results from lab testing under specific configurations. Results might vary with different configurations.
 
-### Azure Event Hubs source
+### Azure Event Hubs source (Basic features mode)
 
-For Azure Event Hubs sources, throughput depends on both the selected throughput level and the number of Azure Event Hubs source partitions. When the Azure Event Hubs source partition count is less than four, throughput is limited by the partition count, regardless of the selected throughput level. The throughput upper limits are as follows:
+For Azure Event Hubs sources (basic features mode), throughput depends on both the selected throughput level and the number of partitions. When the partition count is less than four, throughput is limited by the partition count, regardless of the selected throughput level. The throughput upper limits are as follows:
 
 | Partition Count | Approximate Throughput (up-to) |
 |-----------------|-----------------|
@@ -54,21 +54,16 @@ When the partition count is 4, 16, or 32, throughput depends on the selected lev
 
 ### Streaming connector sources
 
-The throughput for streaming connector sources is up to **30 MB/s**. If you need higher throughput (except for database CDC sources), contact the product group team through [this form](https://aka.ms/EventStreamsConnThroughputRequest).
-
-**Streaming connector sources include**:
-- Azure SQL Database Change Data Capture (CDC)
-- Azure Service Bus
-- PostgreSQL Database CDC
-- MySQL Database CDC
-- Azure Cosmos DB CDC
-- SQL Server on VM DB CDC
-- Azure SQL Managed Instance CDC
-- Google Cloud Pub/Sub
-- Amazon Kinesis Data Streams
-- Confluent Cloud Kafka
-- Apache Kafka
-- Amazon MSK Kafka
+Streaming connector throughput depends on whether the source is partitioned.
+
+For sources with partitions, such as Confluent Cloud for Apache Kafka, AWS Kinesis, and similar services, you can choose from the following throughput levels:
+- **Low**: < 10 MB/s  
+- **Medium**: 10–100 MB/s  
+- **High**: > 100 MB/s  
+
+For non-partitioned sources, such as database CDC sources, throughput is up to **30 MB/s**.
+
+[!INCLUDE [streaming-connector-vnet-supported-sources](./includes/streaming-connector-virtual-network-supported-sources.md)]
 
 ### Other eventstream sources and destinations
 The following table shows the approximate throughput upper limit for custom endpoint sources and different destinations, based on internal lab testing.

docs/data-science/tutorial-data-science-create-report.md

@@ -71,13 +71,57 @@ ms.date: 04/01/2026
 
 1. In the **Connection credentials** section, follow these steps:
 
+    Select the appropriate tab below and follow the steps for your required **Authentication kind**.
+
+    #### [Shared access key](#tab/shared-access-key)
+
     1. For **Connection name**, enter a name for the connection to the event hub.
     1. For **Authentication kind**, confirm that **Shared Access Key** is selected.
     1. For **Shared Access Key Name**, enter the name of the shared access key. For instructions on getting an access key, see [Get an Azure Event Hubs connection string](/azure/event-hubs/event-hubs-get-connection-string#azure-portal).
     1. For **Shared Access Key**, enter the value of the shared access key.
     1. Select **Connect**.
 
-        :::image type="content" source="./media/azure-event-hubs-source-connector/connect-page-1.png" alt-text="Screenshot that shows entered credentials for an Event Hubs connector." lightbox="./media/azure-event-hubs-source-connector/connect-page-1.png":::
+        :::image type="content" source="./media/azure-event-hubs-source-connector/connect-page-1.png" alt-text="Screenshot that shows entered credentials for an Azure Event Hubs connector." lightbox="./media/azure-event-hubs-source-connector/connect-page-1.png":::
+
+    #### [Workspace identity](#tab/workspace-identity)
+    
+    If you want to use **workspace identity** for authentication, complete the following steps before configuring the connection:
+
+    1. Check whether your workspace has workspace identity enabled. If not, go to **Workspace settings > Workspace identity**, and enable Workspace identity.
+
+        :::image type="content" source="../../media/streaming-connector-virtual-network-on-premises-support/enable-workspace-identity.png" alt-text="Screenshot of showing where to enable workspace identity." lightbox="../../media/streaming-connector-virtual-network-on-premises-support/enable-workspace-identity.png":::
+
+    1. Copy the workspace identity **ID** from **Workspace settings → Workspace identity**.
+
+        :::image type="content" source="../../media/streaming-connector-virtual-network-on-premises-support/copy.png" alt-text="Screenshot of showing where to copy workspace identity ID." lightbox="../../media/streaming-connector-virtual-network-on-premises-support/copy.png":::
+
+    1. In your Azure Event Hub, open **Access control (IAM)** and select **Add role assignment**.
+
+        :::image type="content" source="./media/azure-event-hubs-source-connector/add-role-assignment.png" alt-text="Screenshot that shows where to add a role assignment in Azure Event Hub access control." lightbox="./media/azure-event-hubs-source-connector/add-role-assignment.png":::
+    
+    1. Search for and select the **Azure Event Hubs Data Receiver** role, then select **Next**.
+
+        :::image type="content" source="./media/azure-event-hubs-source-connector/data-receiver.png" alt-text="Screenshot that shows selecting the Azure Event Hubs Data Receiver role and choosing Next." lightbox="./media/azure-event-hubs-source-connector/data-receiver.png":::
+
+    1. Under **Assign access to**, choose **User, group, or service principal**.
+    1. Select **Members**, and then enter your workspace name or paste the **ID** you copied to find your workspace. Select **Next**.
+
+        :::image type="content" source="./media/azure-event-hubs-source-connector/add-members.png" alt-text="Screenshot that shows selecting Members and locating the workspace by name or ID." lightbox="./media/azure-event-hubs-source-connector/add-members.png":::
+
+    After completing these steps, return to the connection configuration page and continue:
+
+    1. For **Connection name**, enter a name for the connection to the event hub.
+    1. For **Data gateway**, select the appropriate option based on your Event Hub network configuration:
+        - If your event hub is public, select **none**.
+        - If your event hub is under a private network and you want to connect through a streaming virtual network data gateway, select the streaming vNet data gateway (prefixed with **[Streaming VNET]**). You can select the refresh icon to get the newly created gateway listed.
+
+        > [!NOTE]
+        > If a data gateway is selected, skip the test connection step in this connection creation wizard.
+    1. For **Authentication kind**, confirm that **workspace identity** is selected.
+
+        :::image type="content" source="./media/azure-event-hubs-source-connector/workspace-identity.png" alt-text="Screenshot that shows entered credentials for an Azure Event Hubs connector via workspace identity." lightbox="./media/azure-event-hubs-source-connector/workspace-identity.png":::
+
+    ---
 
 1. For **Consumer group**, enter the name of the consumer group. The default consumer group for the event hub is **$Default**.