Retiring the Fabric data agent custom tool in Microsoft Foundry: move to Fabric IQ
In March 2025, we announced that agents in Microsoft Foundry could use Fabric data agents to answer questions about data in Microsoft Fabric. In the earlier integration, you connected to a data agent by adding it as a custom tool. For details, refer to Consume a data agent in Microsoft Foundry. What’s changing We are deprecating the earlier custom-connection integration. Starting 11/6/2026, that integration will no longer be supported. If your Foundry agents use a Fabric data agent as a custom tool, you must migrate them before that date. The replacement integration (Preview) uses the Fabric IQ (OneLake Catalog) tool, which makes your data available to agents and applications. In the Foundry portal, you can use this tool to add a published Fabric data agent to a Foundry agent. For configuration steps, refer to Consume Fabric data agent from Microsoft Foundry via Fabric IQ. Capabilities with the new integration The replacement integration changes how you find and connect Fabric data agents: Select accessible published data agents from the catalog. In the Foundry portal, the OneLake Catalog shows the published data agents available to you by name, so the connection workflow does not require you to enter a workspace ID or an artifact ID. Connect more than one data agent. A Foundry agent can call multiple data agents, such as separate agents for sales data and customer support data. The earlier custom-connection integration only supported one Fabric data agent. Connect through a Model Context Protocol (MCP) endpoint. MCP is an open standard for how an agent calls a tool. Each selected Fabric data agent is presented to the Foundry agent as a tool through a MCP endpoint. Coming soon, when a data agent answers a question, its response will also include the query it used to generate the answer. What you need to do If any of your Foundry agents use a Fabric data agent as a custom tool, complete the following steps before 11/6/2026: In Microsoft Fabric, make sure to publish your Fabric data agent with a rich and detailed description. Open the agent in the Microsoft Foundry portal. In the Tools section, add the Fabric IQ (OneLake Catalog) tool, and then select your data agent from the catalog. Update the Foundry agent's instructions. Name each data agent and describe the types of questions it should handle. Remove the earlier custom tool and then save the Foundry agent. Test the Foundry agent with representative questions for each connected data agent. If you connect data agents in code, update your implementation to use the Fabric IQ tool. For samples in Python, C#, and JavaScript, refer to Connect agents to Microsoft Fabric with Fabric IQ. Guides to migrate from Fabric Data Agent to Fabric IQ tool Option 1: Migrate in Foundry Portal Navigate to Foundry Portal and open the Tools Catalog, select “Fabric IQ (OneLake Catalog)”. . In the OneLake catalog, filter by “Data agent” type. Select any data agent you want to use, and you are ready to go. Provide instructions to your Foundry agent about how to use Fabric data agent. . Option 2: Migrate in SDK/Rest API After you publish the Fabric data agent, open its Settings and go to the Model Context Protocol tab. This tab shows: Data agent MCP server name MCP server URL (copy this value; you can use in every client) Data agent MCP tool name MCP server tool description Once you get the data agent MCP server URL, proceed with the following steps: Step 1: Get an access token az account get-access-token --resource https://management.azure.com --query accessToken -o tsv Step 2: Create a Foundry connection to the Fabric data agent A connection stores the Fabric data agent endpoint and how to sign in to it. Your agent uses this connection to reach Fabric. Replace each value in angle brackets, then run the request: curl --request PUT \ --url 'https://management.azure.com/subscriptions/<azure-subscription-id>/resourceGroups/<resource-group>/providers/Microsoft.CognitiveServices/accounts/<foundry-account-name>/projects/<foundry-project-name>/connections/<connection-name>?api-version=2025-04-01-preview' \ --header 'Authorization: Bearer <access-token-from-step-1>' \ --header 'Content-Type: application/json' \ --data '{ "type": "Microsoft.MachineLearningServices/workspaces/connections", "properties": { "authType": "UserEntraToken", "group": "AzureAI", "category": "RemoteTool", "target": "<fabric-data-agent-mcp-endpoint>", "audience": "https://api.fabric.microsoft.com/.default", "isSharedToAll": true, "sharedUserList": [], "UseCustomConnector": false, "Credentials": { "ClientId": "fake" }, "metadata": { "ApiType": "Azure" } } }' Main field capabilities: Field Value What it means authType UserEntraToken The agent calls Fabric as the signed in user, with that user's Microsoft Entra token. category RemoteTool The connection points to a remote tool, in this case an MCP endpoint. target Your MCP endpoint The address of your Fabric data agent. audience https://api.fabric.microsoft.com/.default The token is issued for the Fabric API. isSharedToAll true Every user in the Foundry project can use the connection. Credentials.ClientId fake Keep this value as shown. The response includes an id field. This is the full resource ID of the connection. Save it for step 4. It looks like this: /subscriptions/<azure-subscription-id>/resourceGroups/<resource-group>/providers/Microsoft.CognitiveServices/accounts/<foundry-account-name>/projects/<foundry-project-name>/connections/<connection-name> Step 3: Install the Python packages pip install "azure-ai-projects>=2.2.0" azure-identity Step 4: Set the environment variables Variable Where to find it FOUNDRY_PROJECT_ENDPOINT The Overview page of your Foundry project. FOUNDRY_MODEL_NAME The deployment name of your model in your Foundry project. FABRIC_IQ_PROJECT_CONNECTION_ID The full connection resource ID from step 2. Use the full ID, not only the connection name. export FOUNDRY_PROJECT_ENDPOINT="<your-project-endpoint>" export FOUNDRY_MODEL_NAME="<your-model-deployment-name>" export FABRIC_IQ_PROJECT_CONNECTION_ID="<your-connection-resource-id>" Step 5: Create an agent that uses the Fabric IQ tool and ask it a question This script creates a prompt agent with the Fabric IQ tool, sends it one question, prints the answer, and then deletes the agent. import os from azure.identity import DefaultAzureCredential from azure.ai.projects import AIProjectClient from azure.ai.projects.models import PromptAgentDefinition, FabricIQPreviewTool endpoint = os.environ["FOUNDRY_PROJECT_ENDPOINT"] with ( # Signs in with your Azure command line interface account (from az login). DefaultAzureCredential() as credential, AIProjectClient(endpoint=endpoint, credential=credential) as project_client, project_client.get_openai_client() as openai_client, ): # 1. Define the Fabric IQ tool. It uses the connection you created in step 2. fabric_iq_tool = FabricIQPreviewTool( project_connection_id=os.environ["FABRIC_IQ_PROJECT_CONNECTION_ID"], require_approval="never", # The agent calls the tool without asking you first. ) # 2. Create the agent with your model, its instructions, and the Fabric IQ tool. agent = project_client.agents.create_version( agent_name="MyAgent", definition=PromptAgentDefinition( model=os.environ["FOUNDRY_MODEL_NAME"], instructions="Use the available Fabric IQ tools to answer questions and perform tasks.", tools=[fabric_iq_tool], ), ) print(f"Agent created (id: {agent.id}, name: {agent.name}, version: {agent.version})") # 3. Ask the agent a question. Change this to a question about your own data. user_input = "Which customers placed orders above $10,000 last quarter?" response = openai_client.responses.create( input=user_input, extra_body={"agent_reference": {"name": agent.name, "type": "agent_reference"}}, ) print(f"Agent response: {response.output_text}") # 4. Delete this agent version so unused versions don't build up in your project. project_client.agents.delete_version(agent_name=agent.name, agent_version=agent.version) print("Agent deleted") Finally, run the script. python fabric_iq_agent.py Learn more and share feedback We recommend that you move to Fabric IQ as soon as you can. We look forward to improving this integration, and we’d like to hear your feedback. Join the community discussion to share feedback and report issues. For more information, see these articles: Consume Fabric data agent from Microsoft Foundry via Fabric IQ Connect agents to Microsoft Fabric with Fabric IQ
Connect coding agents to Fabric notebooks with FNTK (Preview)
The Fabric Notebook Toolkit (Preview) helps developers shorten the feedback loop between writing code and validating it with live notebook results. It connects coding agents to Microsoft Fabric notebooks through a command-line interface (CLI) that runs in the terminal alongside tools such as Claude Code, GitHub Copilot CLI, and Codex. An agent can inspect the current code, run one cell, review its output, make a targeted change, and run it again without resubmitting the entire notebook—much as a human programmer working interactively. A coding agent uses FNTK from the terminal to interact with a Microsoft Fabric notebook. From code suggestions to notebook execution A coding agent can help write a transformation, but a proposed change is not the same as a working result. When execution happens elsewhere, developers must move code into a notebook, run it, and bring the results or errors back to the agent. FNTK connects those steps so the agent can work with a running Fabric notebook and its execution results instead of relying only on code shared in the conversation. Interactive development and debugging are the central use cases. You can ask an agent to inspect a notebook, investigate a failing transformation, or validate a change using actual execution output. The notebook remains in Fabric and uses Fabric capacity for distributed data processing, while FNTK provides the terminal commands the agent uses to interact with the live session. Run, inspect, and refine one cell at a time FNTK keeps the notebook session active while the agent works, so developers can explore and refine code without repeatedly restarting the workflow. For example, an agent can inspect a schema, adjust a conversion, check several records, and return to the same transformation as new information emerges. This mirrors the iterative way developers work in notebooks and keeps each change grounded in actual execution results. The toolkit brings the operations needed for that workflow into the terminal. An agent can discover the target notebook, read and update its cells, execute code, and retrieve results. Instead of treating every adjustment as a new whole-notebook submission, it can focus on the code being investigated. Find workspaces, notebooks, and lakehouses to establish the context for the task. Read, add, or update notebook cells as the work develops. Execute a cell's code and inspect its output before deciding what to change. Reuse the live session to refine and rerun the relevant code. For example, a transformation may fail because an input column has a different type than expected. An agent can inspect the schema, revise the affected code, and rerun it in the existing session to check the result. This avoids restarting the entire notebook merely to test that change and keeps the investigation focused on the failing operation. Give the agent the evidence to debug FNTK gives the agent the execution evidence needed to determine the source of a failure. It exposes cell output and Spark job state, along with driver logs, executor logs, and logs from Livy, the service that manages Spark sessions. With this context, the agent can investigate the likely source of a problem instead of relying on source code alone. The CLI can return structured JavaScript Object Notation (JSON) responses that identify results, errors, and suggested next actions. An agent can use this information to determine whether execution has finished, retrieve diagnostics, and choose what to investigate next. This shortens the feedback loop between writing code and understanding its behavior while reducing the need to copy outputs and logs manually between tools. FNTK in practice at Microsoft For our data engineering team at Microsoft, FNTK filled a gap between generating code and working with real data. Previously, we used an AI assistant to draft code locally, then copied it into Fabric notebooks to run it and brought the results back into the conversation. With FNTK, we can stay in GitHub Copilot, execute code in Fabric, and use the results to iterate. We started by installing the toolkit, connecting with the appropriate permissions, and asking questions about a data source. Exploratory work quickly became a valuable use case. We could ask for row counts, distributions, or samples, then follow up based on the actual results. From there, we built shared workflow skills and a developer plugin around FNTK, incorporated our engineering standards, and connected the plugin to Azure DevOps through the Model Context Protocol (MCP) to bring user-story context into the same workflow. In practice, this has sped up development and helped us work across multiple user stories, with more of our time spent reviewing changes rather than writing every line ourselves. Copilot handles the reasoning; FNTK provides the connection to live notebook execution. We can still inspect the runs in Fabric, and our existing source-control, quality, and human-review practices remain in place. In practice, this workflow has helped us work across multiple user stories and spend more time reviewing changes instead of writing every line ourselves. Copilot handles the reasoning; FNTK provides the connection to live notebook execution. We can still inspect the runs in Fabric, and our existing source-control, quality, and human-review practices remain in place. Figure: FNTK connects an AI developer agent to live Fabric notebook execution, alongside MCP tools for context and delivery tools for human-reviewed changes. Capabilities with FNTK commands Command or group Usage fntk help Show command guidance and examples. fntk init Install or update coding agent workflow skills. fntk discover Find workspaces, notebooks, lakehouses, and identity information. fntk notebook Manage notebooks, sessions, and default lakehouses. fntk cell Read, edit, and organize notebook cells. fntk code Execute code, retrieve results, and cancel runs. fntk monitor Inspect Spark progress, diagnostics, and resource usage. fntk logs Download driver, executor, Livy, and cell logs. fntk resource Manage notebook resource files and folders. fntk skill Read built-in coding and debugging guidance. fntk server Manage the local FNTK server. Get started To try the workflow, install the toolkit and connect your coding agent by following the FNTK installation and quick-start documentation, including fntk init for workflow skill setup. Then choose an existing development notebook and a focused task, such as inspecting one transformation. Run the code, review the output, and use the result to guide your next change.Power BI September 2026 Feature Summary
As the Power BI, Microsoft Fabric, data, and analytics community gathers in Barcelona for the European Microsoft Fabric + SQL Community Conference, this month is packed with news. From new Copilot and AI experiences to reporting, modeling, data connectivity, developer enhancements, and mobile updates, we're sharing the latest innovations designed to help you build, analyze, and act on your data more effectively. Whether you're joining us in person or following along from afar, this roundup is your guide to everything new across Power BI this month. General Deprecation of Old File Picker experience in Power BI Desktop In April we deprecated the old file picker experience in Power BI Desktop. Starting in October, users using versions of Power BI Desktop from March 2026 or earlier will no longer be able to save and share Power BI files to OneDrive and SharePoint. Please ensure you update Power BI Desktop to keep using this functionality. Copilot and AI Power BI Agentic Experiences (Generally Available) Agentic development in Power BI is real, and it is already delivering value: whether you are starting from scratch with greenfield projects or building upon existing solutions in brownfield scenarios, BI developers can delegate the work to an agent, review the result, and refine it rather than starting from scratch every time. What makes this work is tools and context. Through Work IQ and Fabric IQ, the agent grounds its work in your business context—your data, your semantics, and the way your organization operates. Through skills—published by Microsoft in skills-for-fabric, shared by the community, or personalized by your own team—it also picks up your development context: the modeling and reporting practices, conventions, and workflows your team expects. Through tools such as the Power BI Authoring MCP server and the Power BI Desktop Bridge CLI, the agent can act on that context: reading and changing semantic models and reports directly, reloading its edits in Power BI tools, and reviewing its own work. Point an agent at a requirements document or a screenshot of the layout you want, describe what you need, and the agent can help accelerate the initial authoring pass, which you can then review and refine. rt authoring skills. What's available today The powerbi-authoring plugin in microsoft/skills-for-fabric bundling the semantic modeling and reporting agent skills and tools. It was recently updated to support report authoring for remote semantic models in Power BI service and an expanded range of supported report visuals, including custom visuals. To learn more, refer to the Power BI skills documentation. The local Power BI Authoring MCP server allows the agent to edit semantic models in Power BI Desktop. To learn more, refer to the Power BI Authoring MCP documentation. Power BI Desktop Bridge lets an agent reload changes in a running instance of Power BI Desktop and capture screenshots to check its own work. To learn more, refer to the Power BI Desktop Bridge documentation. Browser-based validation for remote report editing, so the agent can validate a report using a headless browser (an automated browser that runs without a visible user interface). Removing the local Power BI Desktop dependency allows the full authoring loop to run without Power BI Desktop, extending the agentic experience to Mac users and others who do not rely on a local installation. To learn more and get started, refer to the Power BI agentic documentation. Microsoft 365 Copilot is coming to Power BI (Preview) Copilot experiences have emerged across the applications people use every day, bringing AI closer to the work happening in each product. But this can also feel fragmented, with different entry points, separate conversations, and uncertainty about which Copilot agent to use. Our goal is to create a consistent Copilot experience that is available wherever people work. People should not have to leave the application they are using to find it. Microsoft 365 Copilot should be available within their experience, carry relevant context across experiences, and connect to the specialized intelligence of each product. We’re taking an important step toward that goal with a preview of Microsoft 365 Copilot integrated directly into Power BI and Microsoft Fabric. This integration gives users a familiar place to bring together business context and trusted data. Shared conversation history lets them continue a discussion as they move between Microsoft 365 Copilot experiences instead of starting over in each application. ower BI experience, including integration with Work IQ. In Power BI, a question no longer has to stop with the numbers in a report. A sales leader could use Fabric IQ to identify which regions are behind plan, then use Work IQ to connect the results with customer emails, meetings, account plans, and documents. They could summarize the complete picture and even kick off actions and tasks in Cowork, all from inside Power BI. This work context and ability to move directly from insight to action were not available in the standalone Power BI Copilot experience. the Cowork integration into the Power BI experience, highlighting the full Microsoft 365 experience embedded into Power BI. A new setting in the Fabric admin portal will determine whether this integration is available by enabling or disabling its entry point in the navigation pane. It will be off by default, giving administrators time to evaluate the experience before introducing it to their organization. Administrators can enable it when they are ready and disable it if they need to pause access. This setting is independent of the existing setting for standalone Power BI Copilot. If both settings are enabled, users with a Microsoft 365 Copilot license will see entry points for both experiences, and standalone Power BI Copilot will continue to appear on Home. If standalone Power BI Copilot is disabled and Microsoft 365 Copilot is enabled, users will see only the Microsoft 365 Copilot entry point. During the initial preview, the integrated experience is available only to users with a Microsoft 365 Copilot license. These users receive the full Copilot Chat experience in Power BI and Fabric, bringing together Fabric data through Fabric IQ and relevant Microsoft 365 context through Work IQ. Existing permissions and governance controls continue to determine which content each user can access. Support for a broader Fabric audience will follow. This expansion will give Fabric users without a Microsoft 365 Copilot license access to an integrated Copilot experience tailored to their available capabilities. The experience may differ from the full licensed offering, but it will extend Fabric IQ and conversational data experiences to more types of Fabric users. The preview will roll out with the setting turned off by default, with administrators choosing when to enable it for their organization. This is an important step toward one Copilot that meets people wherever they work, connecting the context surrounding each decision with trusted business data. To learn more, refer to the Copilot in Power BI documentation. Power BI data answers in Microsoft Copilot Chat (Generally Available) Users can find and ask questions about Power BI reports and semantic models directly from Microsoft 365 Copilot. They can search for relevant Power BI content, attach a report or provide a link, and ask questions in natural language. Powered by Fabric IQ, the experience uses the data, measures, relationships, and business logic already defined in Power BI to provide grounded answers. Users can also bring Power BI insights into conversations that include their Microsoft 365 work context, such as emails, meetings, and documents. Microsoft 365 Copilot now supports Microsoft Purview Data Loss Prevention (DLP) policies and information protection controls for this experience. This helps organizations adopt conversational analytics while applying their existing policies for identifying, classifying, and protecting sensitive data. The experience continues to respect existing Power BI permissions and Microsoft 365 governance controls. Users can only access Power BI content they are authorized to view, and administrators retain control over whether Fabric data is available in Microsoft 365 Copilot. Organizations can use Power BI data answering in Microsoft 365 Copilot with production support and service commitments. This milestone makes it easier for users to bring trusted business data into the flow of work without leaving Microsoft 365 Copilot or creating a separate agent. To learn more, refer to the Fabric IQ in Microsoft 365 Copilot Chat (Frontier) documentation. Fabric IQ Plugin in Microsoft 365 Copilot Cowork (Generally Available) Power BI data is available where multi-step work happens, so a business question, the analysis behind it, and the follow-up it triggers stay in one place. To ground a task in Power BI, users can search for relevant content, attach a report, or paste a link to a report or semantic model. Questions are answered using the data, measures, relationships, and business logic already defined in the semantic model. Coverage now extends to reports published through organizational apps, with improvements to content discovery, large model handling, schema selection, Verified Answers, and row-level value search. The plugin also carries forward the governance controls organizations rely on in Power BI: Existing access controls: Power BI permissions and row-level security determine which reports and semantic models each user can reach. Citations and sensitivity labels: Responses cite the Power BI content behind an answer and surface the Microsoft Purview Information Protection label applied to it, so users can see where a number came from and how it is classified. From insight to action: With Fabric IQ, users can analyze Power BI reports using natural language, uncover key findings, and put those findings to work right away in Cowork. Whether they are summarizing business performance, investigating a trend, or answering a data question, Fabric IQ turns trusted organizational data into outcomes. To learn more about supported scenarios, setup, and current considerations, refer to the Fabric IQ in Microsoft 365 Copilot Cowork documentation. For licensing and pricing information, refer to Microsoft 365 Copilot plans and pricing. Build Fabric Apps more easily in GitHub Copilot App (Preview) Fabric Apps were introduced in June as an AI-first way to build custom web apps with Microsoft Fabric as the backend. This month we’re excited to share that building these Fabric Apps on your governed data has become even easier with the GitHub Copilot App, which gives you a natural language chat interface instead of a command-line interface. . To try it out, start by downloading the GitHub Copilot App. Then navigate to Customize > Plugins > Rayfin, and select Install to enable our dedicated plugin: Rayfin plugin in GitHub Copilot App. Once the plugin is enabled, simply start a new chat and work with Copilot to help plan your Fabric data app. After you review and approve the plan, ask the agent to execute it and specify the workspace where you want to deploy the app. . We look forward to seeing the amazing apps you build! Check out our community gallery for inspiration. Enhanced Report Consumption Copilot with Fabric IQ When viewing a Power BI report, you can enable Fabric IQ from the Copilot pane to better answer complex questions about your data. This enhancement merges existing Fabric IQ capabilities into Power BI Copilot report viewing mode, allowing Copilot to use a multi-step approach to evaluate data in the semantic model and refine its approach when generating a response. . With Fabric IQ enabled, Copilot can: Provide better answers to questions about your data based on detailed reasoning capabilities directly into the report viewing experience. Show reasoning steps to help you understand how Copilot provided an answer. Generate Fabric visuals to help you further analyze your data. Answers can include column, bar, stacked, line, area, pie, donut, scatter visuals. Provide the underlying DAX query and data table used to generate a response. . To learn more, refer to the Ask Copilot questions about your data documentation. New control for Copilot access to semantic models Power BI now provides semantic model editors with greater control over how Copilot can be used with their data. Users with write permissions can block read-only users from accessing a semantic model through Copilot and Microsoft’s remote MCP tools, supporting governance, compliance, and access-control requirements. The setting applies across Copilot experiences that query the semantic model, including Power BI Copilot experiences; Microsoft 365 Copilot Chat and Cowork; and data agents, remote Fabric MCP servers, and Power BI MCP servers. When disabled, users with read-only access won't be able to access that semantic model through these experiences. By default, the setting remains enabled to preserve the current behavior. This setting appears in the Copilot section with the following description: “Allow any person with read-only access to use AI (i.e. Copilot, Microsoft’s MCP tools) on the model and its related reports.” Setting that allows people with read-only permission to use AI experiences with the semantic model and its related reports. This update gives organizations a clear and enforceable way to control AI access to semantic model data while maintaining flexibility for semantic model editors. To learn more, refer to the Use Copilot with Semantic Models in Power BI documentation. Remote Power BI Authoring MCP Server (Preview) The Power BI Authoring MCP enables AI applications to create and modify semantic models in Fabric workspaces, without installing or maintaining anything local. This release also unblocks Mac users that want to use the Power BI Authoring MCP, because the server runs remotely and does not require local binaries. Use the following endpoint in AI applications such as GitHub Copilot App: https://api.fabric.microsoft.com/v1/mcp/powerbi/authoring The local Power BI Authoring MCP server, released in November 2025, remains generally available. It remains an option when an agent needs to work with Power BI Desktop, Power BI Project (PBIP) or TMDL files on disk. The local and remote servers support the same authoring capabilities. We recommend using the remote server when local development is not required, such as when creating a Direct Lake semantic model in a Fabric workspace. In the following example, the AI agent uses one tool call to create a Direct Lake semantic model connected to a Fabric Lakehouse: To learn more, refer to the Power BI Authoring MCP server documentation. Reporting New Report Ribbon on the web (Preview) The Report ribbon is now available on the web, providing a consistent experience for report consumption and authoring that more closely matches Power BI Desktop. The ribbon organizes commands into clear, easy-to-navigate tabs, making it easier for users to discover features, interact with reports, and complete editing and authoring tasks directly in the browser. It also introduces the new Design ribbon and additional capabilities that were previously unavailable on the web, such as Format Painter. This milestone brings greater consistency to Power BI report-authoring experiences and marks an important step toward a seamless, end-to-end reporting workflow on the web. As web authoring capabilities continue to expand, users will be able to accomplish more of their reporting tasks directly in the browser, enabling a more streamlined, productive workflow. Enable the Try the new ribbon (preview) at the top of the page to explore the new experience. You can disable the setting to return to the previous ribbon. To learn more, refer to the Report Ribbon on the web documentation. Live Visual Sharing in Outlook on the web (Preview) For years, sharing data through email has been stagnant. You could send a screenshot, but it immediately became stale. You could attach a report link, but recipients had to leave their workflow to find the context they needed. And every follow-up question often meant another screenshot, another export, or another email thread. Now, we're bringing a new way to share Power BI insights directly in the flow of work with Live Visual Sharing in Outlook. With Live Visual Sharing, you can insert a Power BI visual into Outlook on the web and share it as a live, interactive experience. Recipients can view and interact with the visual directly in Outlook while staying connected to the latest underlying data. When a live visual is shared, Outlook renders an interactive version of the Power BI visual. Rather than embedding a static image, the experience remains connected to the source report and retrieves current data when viewed. Recipients can: Interact with the visual directly in Outlook on the web. Access tooltips and explore data points. See filters and slicers applied at the time of sharing. View refresh information and data context. Navigate back to the source report when deeper analysis is needed. The result is a richer conversation around data, directly within Outlook. Sharing a live visual is simple: Open a Power BI report in the service. Select a visual to share, then select More options in the upper-right corner. Select Share. Select Link to this visual. A pop-up will appear asking if you want to use the current view (apply existing filters and slicers) or not. After selecting Create Link, the link is copied to your clipboard. Open Outlook on the web. This experience is not available in the Outlook desktop app. Create a new email. Paste the link in the email. The visual renders in loop after a few seconds! You can also share the same link in a Loop page, and the visual will also be rendered. By combining Power BI, Outlook, and Loop-powered experiences, we're bringing data closer to where collaboration happens. Live Visual Sharing can support more informed, data-driven conversations in Outlook. To learn more about Live Visual Sharing in Outlook, refer to the Share live Power BI visuals documentation. More control and context in Power BI Home (Generally Available) Power BI Home now provides additional context and personalization options to help users discover the right content more efficiently. With new freshness indicators and customizable list views, you can quickly identify the most relevant reports and semantic models, prioritize the metadata that matters most, and tailor the experience to fit your workflow. Find the right content more easily Discover the right content more easily with new Data Refreshed and Modified Date columns in Power BI Home. By bringing the freshness signals already familiar from workspace list views into Home, users can more easily spot recently updated reports and semantic models, distinguish between similar items, and quickly find the content that matters most. Whether you're checking for the latest business updates or deciding which refreshed report to open, you can navigate with greater confidence and less guesswork. Customize your Home list view Not every user, team, or organization relies on the same signals to discover content. With Column Options, you can show or hide metadata, drag and reorder columns to prioritize the information you use most, and reset to the default view at any time. Whether you rely on freshness signals, endorsements, sensitivity labels, or location information, you can tailor Home to match your workflow. To learn more, refer to the Power BI Home documentation. Set and reset column widths in table and matrix (Generally Available) You can now right-click a column header in a table or matrix and select Set column width to specify its width in pixels. This option helps report viewers improve readability and fit the information they need within the available space, without changing the report author’s design or affecting other viewers. Changes made by viewers last only for the current viewing session. To restore the width defined by the report author, right-click the column header and select Reset column width. To learn more, refer to the Adjust column width in Power BI tables documentation. Visual calculations for small multiples (Generally Available) You can now use visual calculations in visuals with small multiples. Create calculations such as moving averages and compare trends across categories without adding separate measures to the semantic model. In the visual matrix, the field in the small multiples field well appears on the ROWPAGES axis. You can select ROWPAGES by itself or combine them with ROWS and COLUMNS to control how the calculation moves through the visual. e: Create a moving average visual calculation for a line chart divided into small multiples by country. To learn more, refer to the Visual calculations overview documentation. Design ribbon for Power BI reports (Generally Available) The new Design ribbon brings report styling tools together in one location. Apply a built-in or organizational theme, open the Theme pane to customize report-wide defaults, or import and download theme files directly from the ribbon. You can also quickly open Page size, Page background, and Wallpaper settings for the current page. Two new built-in themes, Dark and Soft dark, use the new Versa color palette and styling that closely follows the Fluent 2 base theme. These themes let you design against a dark report canvas and return to the default light theme before publishing if needed. To learn more, refer to the Use the Design ribbon in Power BI documentation. Slider color options for Slicer visual (Generally Available) Match your slicer's slider to your report design. The Slicer visual's Between, Before, After, and Date picker settings include a Slider format section where you can turn the slider on or off, set the handle style, and choose colors for the handles, selected range, and track. Handle options adjust based on the Responsive layout toggle (General > Properties > Advanced options). When Responsive is on, handles appear as circles with Fill color, Border, and Size. When Responsive is off, handles appear as rectangular bars with Border, Width, and Height. To learn more, refer to the Slicer visual in Power BI documentation. Translytical task flows show user date function (UDF) information in button action (Generally Available) The formatting pane now displays the Workspace, Function set, and Data function assigned to a data function button. These read-only details help you confirm that the button references the intended function without reopening the OneLake catalog. This information is especially useful when you review or troubleshoot reports that use several functions or connect to functions across multiple workspaces. To learn how to configure data function buttons, refer to Create a data function button in Power BI. Format pane headers stay visible as you scroll (Generally Available) Expanded section headers now remain visible at the top of the Format visual pane as you scroll through their settings. This update helps you keep track of the section you’re editing, especially when a section contains many formatting options. For example, when you scroll through color settings, the Colors and Data colors headers remain visible for context. To learn more about formatting visuals, refer to Get started formatting report visualizations. Pick the axis initial scroll position (Generally Available) Categorical axes—like month, year, or product name—show a scrollbar when there are more categories than fit in the visual. Until now, the scrollbar always started at the beginning of the data, so the oldest values displayed first. This month, you can set Initial scroll position to End on the axis Layout section, and the visual loads with the most recent data in view. Users can still scroll back to see earlier history. The option is available on stacked, clustered, and 100% stacked bar and column charts; line, area, stacked area, and 100% stacked area charts; line and stacked column and line and clustered column charts; ribbon, waterfall, and scatter charts with a categorical X-axis. Initial scroll positionset toEndon the X-axis. The scrollbar is positioned on the right, showing the most recent months first. To learn more, refer to the Customize x-axis and y-axis properties documentation. Azure Maps geocoding improvements (Generally Available) Large reference layers now render correctly when you filter your report. Reference layers add geographic context using points, lines, and polygons, such as locations, routes, postal codes, sales territories, and administrative regions. The Azure Maps visual renders up to 30,000 reference-layer features at a time. Previously, when a reference layer exceeded this limit, features excluded from the initial view could remain missing even after a report filter narrowed the data to a smaller area. . . . Now, Azure Maps re-evaluates the reference-layer features whenever report filters are applied. The unfiltered view is still limited to 30,000 features, but a filtered view accurately displays the relevant features, up to that limit. On-object interaction improvements (Preview) Based on your feedback during preview, we're introducing the first in a series of on-object interaction improvements, designed to make quick edits to your visuals easier. This month we're refining how you add data and manage some visual elements. The Build flyout now opens only when you select Add data, helping you stay focused when dragging and dropping data directly onto a visual or into the Build pane. We’ve also separated Title and Subtitle in the Add to your visual flyout, so you can manage them independently. In addition, title and subtitle are now turned off by default for matrix, table, and card visuals in reports that use the latest base theme. New reports automatically use the latest base theme. For existing reports, select Update theme in the Theme pane to apply this change. You can turn either element back on when needed. to your visual menu. To learn more, refer to the using on-object interaction with visuals and updating to the latest base theme documentation. Support for Fabric Apps in Org apps (Coming Soon) Soon, users will be able to include generative Fabric Apps alongside the reports, dashboards, notebooks, and other content in your Org Apps. For example, imagine you want people to rate the content in an Org App and share feedback about what would make it more useful. You can create a Fabric App for that experience and add it directly to the Org App. Whether you’re helping people explore what could happen next, act on an insight, or complete a specialized workflow, Fabric Apps give you a flexible new way to build for your audience without sending them somewhere else. Together, organizational apps and Fabric Apps provide a way to bring enterprise reporting, content, and tailored experiences together for your organization. Learn more about org apps and item types supported, including Fabric Apps. Changes to Power BI subscription email delivery Power BI is updating the platform used to deliver subscription emails as part of ongoing improvements to delivery reliability and long-term service health. As eligible subscriptions move to the updated platform, customers may notice changes to the sender identity and email formatting. Each subscription transfers automatically when it moves. Customers do not need to recreate it or change its schedule or recipients. After selecting Subscribe for an item, an information icon identifies a subscription that has moved to the updated email delivery platform. Learn more about changes to Power BI subscription emails. Increased Export to Excel row limits (Coming Soon) Many users export Power BI data to Excel for further analysis. This month, we're increasing the maximum number of rows that can be exported from table visuals using Data with current layout from 150,000 to 500,000. This increase helps you analyze larger datasets in Excel with fewer export operations. s. To learn more about exporting data from Power BI visuals, refer to Export data from a Power BI visualization. Value selection in Narrative visual We are replacing the Q&A natural language experience for creating dynamic values in the Narrative visual and text box with a more deterministic UI. This is due to the ongoing deprecation of the Q&A tool, and also based on feedback that it was hard to deterministically pick exact values through the Q&A experience. Authors can now create and edit custom values through a direct, deterministic UI: select a column or measure, apply any valid summarization, and add any type of filter. Auto-generated values are unchanged, and values previously authored with Q&A remain available as read-only. To learn more, refer to the Create Smart Narrative documentation. Modeling Create and edit DirectQuery semantic models in the Power BI service DirectQuery semantic models can now be created and edited directly in the Power BI service, eliminating the need to download and use Power BI Desktop. Previously, building models that query data sources in real time meant downloading Desktop, authoring the model, and publishing it to the web. Now, web-based modeling gives your team the freedom to: Work from any device: Mac, Linux and Windows users can now author DirectQuery models straight from the browser. Collaborate in real time: Multiple team members can work on the same semantic model simultaneously. Ditch the desktop loop: Skip the download-edit-republish cycle and update your models instantly in the service. Capabilities on the web Start from scratch: Connect to a supported data source and choose DirectQuery mode. Figure: Power Query interface displaying data source connection settings, with DirectQuery selected as the data connectivity mode. Full modeling control: Open existing DirectQuery models to manage relationships, write DAX and tweak table/column properties. : Model View in Power BI Service showing tables, relationships, and the properties panel for editing a DirectQuery semantic model. Flexible storage modes: Easily switch table storage modes from DirectQuery to Import or Dual if needed. : Properties pane that allows user to switch table storage modes from DirectQuery to Import or Dual. Thanks to this new capability, composite models are also available on the web! Head to the Power BI service and open one of your DirectQuery semantic models or create a brand-new one. We'd love to hear what you think! Learn more in the DirectQuery documentation. Approximate Distinct Count comes to Import and Direct Lake (Preview) Distinct count is one of the most resource-intensive operations in any analytical engine. As data cardinality grows, so do CPU and memory usage, often slowing down your interactive reports. APPROXIMATEDISTINCTCOUNT already offered a faster alternative for DirectQuery sources such as Azure SQL, Synapse, BigQuery, Databricks, and Snowflake. This month we're extending support to Import, Direct Lake on OneLake, and Direct Lake on SQL semantic models. DAX = APPROXIMATEDISTINCTCOUNT(<columnName>) : Model View in Power BI Service showing tables, relationships, and the properties panel for editing a DirectQuery semantic model. How it works and when to use it Instead of scanning every single unique value, APPROXIMATEDISTINCTCOUNT calculates a highly accurate estimate of unique values with a standard error rate of ~1.6%. In return, you get faster query execution times. Best for high-cardinality columns: Use it on columns with millions of unique values (such as transaction IDs, user IDs, or timestamps) where an approximate result can reduce the resources required to calculate distinct values. Keep standard DISTINCTCOUNT for low-cardinality data: Stick to exact counts for smaller attributes like Country, Category, or Status, where standard calculation is already fast, or for financial metrics where absolute precision is required. Swap APPROXIMATEDISTINCTCOUNT into your heaviest measures today and let us know how much speed you gain on your models! Learn more in the APPROXIMATEDISTINCTCOUNT documentation. Accelerate text searches in your semantic models with String Indexing (Preview) String Indexing for Power BI semantic models is coming soon! If your reports rely heavily on text fields, such as product names, customer comments, or long descriptions, you’ve likely faced slow performance when users search or filter on large semantic models. String Indexing is designed to improve text-search performance in supported semantic models. Why String Indexing? Common text operations like DAX functions (SEARCH(), CONTAINSSTRING()) and advanced text slicers in reports traditionally scan data row-by-row. As datasets grow, these scans slow down query times. String Indexing builds specialized indexes so the engine can locate matching text more efficiently, which can improve report responsiveness for both Import and Direct Lake semantic models. Indexing modes You can configure String Indexing via model metadata to fit your workload needs: Auto Mode: Automatically builds the index on the first query execution for a qualifying column. The index does not persist in storage; it remains in memory only while the column is loaded and will be rebuilt on subsequent queries if evicted. Ideal for smaller models. Full Mode: Builds and persists the index during scheduled data refreshes. While refresh times and model size may slightly increase, users enjoy consistently fast query performance right out of the box. Get started String Indexing requires a semantic model running at 1707 compatibility level. During this initial preview phase, indexing can be configured through TMDL on the web or XMLA metadata. Desktop support and a dedicated UI experience within Power BI are planned for a future release to simplify setup. Check out the String Indexing documentation for setting up instructions, prerequisites, and guidance on choosing the right indexing mode for your models. We look forward to seeing how this feature helps make text-heavy reporting experiences faster and more responsive! Introducing Full-Text Search in Power BI Semantic Models (Preview) Full-Text Indexing for Power BI semantic models is coming soon! While traditional string searches look for exact character matches, Full-Text Indexing enables a more intelligent search experience based on the meaning of words and natural language concepts. This capability powers two new DAX functions, TEXTCONTAINS() and TEXTSIMILARITY(), making it easier than ever to analyze large volumes of textual data directly in your semantic models. Why Full-Text Indexing? Business data increasingly contains rich text content such as customer feedback, support tickets, product descriptions, incident summaries, and knowledge articles. Searching this data using traditional string matching often requires exact matches and can miss relevant results due to differences in spelling, word forms, or phrasing. Full-Text Indexing builds a specialized search index at the column level, allowing semantic models to understand words rather than just character sequences. This enables advanced search experiences such as: Finding related words through linguistic analysis (for example, run, running, and runs) Matching singular and plural variations automatically Ignoring common stop words such as "the" or "and" Supporting phrase-based searches Tolerating minor spelling mistakes through fuzzy matching Enabling semantic similarity search scenarios Getting Started FTS Indexing requires a semantic model running at 1708 compatibility level. During this initial preview phase, indexing can be configured through TMDL on the web or XMLA metadata. Desktop support and a dedicated UI experience within Power BI are planned for a future release to simplify setup. New DAX functions Full-Text Indexing is required for two new DAX functions: TEXTCONTAINS() Returns TRUE when a text value matches a search expression using full-text search semantics rather than simple substring matching. It supports multiple search modes, including keyword matching, phrase matching, and fuzzy matching for typo tolerance TextContains(<WithinTextColumn>, <SearchText>, [MatchMode]) Example: "Show me all reviews that mention spa." The review either matches or it doesn't. TEXTSIMILARITY() Returns a relevance score indicating how well the text in <WithinTextColumn> matches <SearchText>. It enables similarity-based text comparisons, helping identify records that are semantically related even when they do not contain the exact same words. TextSimilarity(<WithinTextColumn>, <SearchText> [, <MatchMode>]) Example: "Show me the 10 reviews most relevant to 'good breakfast'." Reviews mentioning both terms prominently rank higher than reviews that only partially match. Learn more about these two new DAX functions (TEXTCONTAINS, TEXTSIMILARITY) in our documentation. Full-Text Indexing is supported for both Import and Direct Lake models, with Full mode recommended (during Preview) for the best out-of-the-box performance and user experience. Refer to the Full Text Indexing documentation to learn more about prerequisites, configuration options, and setup instructions. Data connectivity New Power Query editor (Preview) Since May of this year, the modern Get Data experience has been available in preview inside Power BI Desktop; the feedback we've gathered from that rollout has directly shaped what comes next. Your reports of friction points, missing capabilities, and rough edges between the legacy and modern interfaces went straight into the backlog, and this month that work pays off: the full modern Power Query Editor experience moves into preview. It's the same trusted engine, wrapped in a completely refreshed editing surface — and we're still closing the remaining gaps with the legacy UI as we go. Let's explore six key reasons to enable and try this feature. Modern Get Data - The same connector browsing and search experience you already know from Fabric Dataflow Gen2 — now in Desktop. Searchable, categorized, and designed to make connectors easier to find and navigate than the legacy dialog. Try it: Connect to a new data source and browse the OneLake catalog. To learn more, refer to the Data sources in Power BI Desktop documentation. Dark theme, end to end - No more jarring light-mode panes breaking your flow. The legacy editor only partially supported dark theme. The modern editor applies it consistently across the preview grid, dialogs, and other parts of the editing surface. Folding visibility - Stop guessing whether your query folds. Step folding indicators mark each step as folding or not, and the new query plan shows exactly how the engine intends to evaluate your query — and where folding breaks down. Try it: Open the query plan on a query with several steps and find where folding stops. To learn more, refer to the Understanding Query Evaluation and Query Folding in Power Query documentation. Diagram view - See how your queries relate to each other visually, instead of reverse-engineering it from names. A gamechanger for files you've inherited or models with long reference/merge chains. Try it: Open a file with referenced queries and trace a merge visually. To learn more, refer to the Diagram view documentation. New capabilities, now on Desktop - Features that used to live only on the web are now here: rank column, cluster values, schema view, query script view, plus upgrades to transforms you already use daily. Try it: Add a rank column or cluster values on a messy text column. To learn more, refer to the Rank column and Use Schema View documentation. Export a Power Query template - Capture your solution at a point in time and take it with you. Export a template from Desktop and import it straight into a Dataflow Gen2 — queries, steps, and structure intact. Try it: Export a template from a multi-query file and import it into a Dataflow Gen2 to compare. Figure: Screenshot of the Export template experience in the Power Query editor. To learn more, refer to the Power Query template documentation. Ready to try it? Enabling the modern Power Query experience takes just a few seconds — no restart required: Open Power BI Desktop. Locate: File > Options and settings > Options. Under Preview features, check New Power Query experience. Open (or reopen) the Power Query Editor — you're now in the modern experience. Once you've had a chance to explore this new user interface and its experiences, we want to hear from you. Share your thoughts on the new Power Query experience in the Fabric Community — what's working, what's missing, and what would make it a compelling alternative from the legacy editor. Every comment helps shape what ships next. Microsoft Outlook Mail connector (Preview) We're introducing the new Microsoft Outlook Mail connector (Preview) for Power BI Desktop. Built on the Microsoft Graph API, it lets you read your mailbox data — folders, messages, and attachments — using an organizational account. This is the first in a set of new connectors we'll be releasing as a modern alternative to the Exchange Online connector. The initial preview is scoped to Mail in Power BI Desktop. Additional connectors for Calendar Events, Contacts, and Tasks are on the roadmap, along with Excel and Power BI service / Fabric Dataflows Gen2 support. Scheduled refresh isn't supported yet — this applies to both the Power BI service and refreshes routed through the on-premises data gateway. To try it, enable the Microsoft Outlook Mail preview feature under Options in Power BI Desktop, then open Get Data → Online Services → Microsoft Outlook Mail and sign in with your organizational account. To learn more, refer to the Microsoft Outlook Mail connector documentation. More flexible Data Privacy Firewall query patterns in Power BI Desktop (Preview) Power BI Desktop now allows Data Privacy Firewall partitions that reference other partitions also access data sources when the privacy levels of the involved data sources are compatible. Introduced in the July 2026 release and enabled by default, this preview setting aligns Power BI Desktop with Power Query Online, gateways, and the Power BI service. This change supports common query patterns in which one query returns a value that another query uses to build a web request, SQL statement, file path, or other data source request. These patterns can now run without refactoring queries or duplicating logic solely to satisfy the previous partition restriction. The Data Privacy Firewall remains active. Power Query continues to evaluate the privacy levels of the involved data sources before combining data, helping authors use more flexible query designs while preserving privacy protections. To learn more, refer to the Allow data privacy firewall partitions that reference other partitions to also access data sources documentation. Mobile Mobile page indicator (Generally Available) Know immediately which report pages are mobile-ready, without clicking through them. Each page that has a mobile layout shows an icon on its page tab in the footer, so you can spot your mobile coverage instantly. Pages without a mobile layout stay unmarked, making it easy to see what's left to optimize. The icon is coming soon to both Power BI Desktop and the Power BI service. In Power BI Desktop, it appears in both desktop layout view and mobile layout view, so you can track mobile coverage right where you work. In the Power BI service, it appears in mobile layout view. Page tabs marked with an icon to show which pages have a mobile layout. To learn how to build mobile-optimized reports, refer to the Mobile-optimized Power BI reports overview documentation. Contact details in Info drawer (Generally Available) We’re making it easier to connect with the right people from the Power BI Mobile app. You can now find contact details in the Info drawer for reports, apps, and dashboards, and email the relevant contact directly from your phone. To learn more about Power BI in mobile, refer to the What Are the Power BI Mobile Apps? documentation. Developers and APIs Power BI Projects (PBIP) (Generally Available) Since entering preview in 2023, PBIP has changed how developers build and manage Power BI content. It stores report and semantic model definitions as human-readable text files, turning a Power BI solution into a codebase. Developers can search these files, track changes in Git, review diffs in pull requests, automate updates with scripts and pipelines, and use AI agents to work with models and reports. Changes remain visible and reversible, which makes team development easier and safer. Recent improvements have made the workflow more practical. These include opening a project directly in Visual Studio Code and detecting external file changes in Power BI Desktop. New underlying formats capabilities: Tabular Model Definition Language (TMDL) for semantic models. Power BI Enhanced Report Format (PBIR) for reports. PBIR is now the default report format for PBIX and PBIP files. New reports use it from the start. When you edit a report that uses the legacy format, Power BI tools automatically convert it to PBIR. To learn more, refer to the Power BI Projects documentation. New Purge Data option for Fabric APIs The new Purge Data option for Fabric APIs gives developers and administrators greater control over programmatic semantic model deployments. This capability allows users to explicitly approve deployments that may require removing existing data, helping unblock scenarios such as schema or data type changes that previously failed validation checks. Designed with both safety and automation in mind, purgeData keeps data protection safeguards enabled by default while giving developers the flexibility they need to manage complex deployment scenarios at scale. To learn more, refer to the Purge Data documentation. Other Data updated and sensitivity fields of Power BI report in Microsoft Teams preview card (Coming Soon) Power BI report previews in Microsoft Teams will soon surface the report's last refresh date and sensitivity label. These additional signals will help users quickly assess the freshness, relevance, and classification of a report before opening it. To learn how Power BI content is displayed in Microsoft Teams, refer to the Link preview cards in Microsoft Teams chats and channels documentation. Artifact info header for Power BI tabs in Microsoft Teams (Generally Available) Power BI content in Microsoft Teams tabs now includes the artifact info header, giving users the context they need without leaving their flow of work. Available for reports, apps, organizational apps, dashboards, and scorecards, the header brings key artifact information and actions together in one place. The new header displays the artifact name and sensitivity label and provides an entry point to artifact metadata. You can open the artifact in the Power BI personal app in Teams, open it in a browser, and refresh the tab. Tab owners can control their experience with the new Show info header setting on the tab settings page. The setting is 'on’ by default and can be turned off when a more compact tab experience is preferred. To learn how to add Power BI content as a tab in Microsoft Teams, refer to the Embed Power BI reports in Microsoft Teams documentation. Third-party custom visuals graphomate matrix The graphomate chartrix is a brand-new charting visual, and it offers various improvements over graphomate charts. First and probably most obvious is the new interactive user interface which enables users to define most elements of the chart layout directly on the visual. To keep this interface effective and uncluttered it focuses on the most important parameters of your chart. Users can now create combination charts which were simply not possible before. There are also new chart types like the variance waterfall, which automatically renders from one measure and two dimensions. Of course, the graphomate chartrix is IBCS® certified. With the latest release an XFL editor has been introduced, which helps you write scripts to format the chartrix far beyond the possibilities of the regular option set. XFL scripts also provide a way to conditionally format the visualization, dynamically changing properties in relation to the actual data. This new visual is provided free of charge to existing customers of the graphomate charts. All development efforts will be focused on making the graphomate chartrix the best IBCS® charting visual there is. Video Link: graphomate chartrix for Microsoft Power BI AppSource Link: graphomate chartrix Sample Report Link Drill Down Bubble PRO by ZoomCharts Drill Down Bubble PRO is a Power BI visual designed for exploring relationships, trends, and outliers across multiple dimensions of data. It adds richer exploration, selection, and analytical capabilities directly inside the visual. Key features include: Multi-level drill down to explore data hierarchies from a high-level overview to detailed insights. Pan and zoom controls to navigate dense datasets and focus on specific clusters or individual data points. Area and lasso selection to quickly isolate groups of bubbles and use selections for interactive analysis and filtering. Dynamic regression lines that recalculate as the displayed data changes, making trends and relationships easier to identify. Line and area thresholds to highlight targets, ranges, exceptions, or other important business benchmarks. Area shading to visually group and compare selected segments of data. 200+ formatting settings to tailor the chart to different reporting and branding needs. Figure: Drill Down Bubble PRO demonstrates multi-level drill down, panning, and zoom controls for exploring data from high-level patterns to detailed points. These capabilities make Drill Down Bubble PRO especially useful for customer segmentation, sales analysis, portfolio analysis, performance monitoring, and other scenarios where users need to actively explore patterns rather than simply display them. Get on Markeplace Commenting in view mode: introducing Zebra BI Tables+ & Charts+ It's month-end. A regional manager opens the report, spots a variance in their region, and knows exactly why it happened. But the report isn't theirs to edit, so the explanation ends up where it always does: a screenshot pasted into PowerPoint, emailed around, detached from the number that prompted it. That's the collaboration gap. Your report shows what happened; the why and the what next leave the report and get lost. Meet Zebra BI Tables+ and Charts+: two new visuals that keep everything existing Zebra BI Tables and Charts offer and add a built-in collaboration layer. Report viewers can add comments directly on a data point, in view mode, with no Power BI edit access. Each comment is anchored to the exact figure and stored in your own SharePoint file. Zebra BI stores nothing. ting files from SharePoint, and collaborate directly in view mode. So instead of screenshotting insights into PowerPoint or maintaining a custom workaround, the whole team explains and decides right on the report. Instead of slides or emails, the next steps can be defined directly on the spot. Stop losing good decisions to a missing context. Start making them on the report. Try Zebra BI Tables+ and Charts+ free for 14 days on AppSource. Network Graph by Powerviz The Powerviz Network Graph is a visual representation of Nodes and the relationships or connections between them. Its primary purpose is to illustrate how different entities are related or interact with one another. Key Features Line Options: Customise node links with different styles and colors. Node Shape: Choose from various shapes, icons, or upload your own. Labels: Display categories, values, and percentage labels. Data Colors: Access 30+ color palettes, including color-blind-safe options. Fill Patterns: Highlight nodes with patterns or upload your own. Conditional Formatting: Highlight outliers based on measures or category rules. Clusters: Group related nodes to reveal patterns. Relationships: Analyze node connections at a glance. Animation: Add pop-ups and highlight effects. Import/Export: Save and reuse themes easily. Toolbars: Gain advanced control over the graph. Other features included are Sorting, Ranking, Lasso / Reverse Lasso, Grid View and more. Business Use Cases Customer Relationship Management (CRM), Supply Chain Optimization, Social Network Analysis (SNA). Resources Try Network Graph visual for FREE from AppSource Check out all features of the visual Step-by-step instructions YouTube Video Learn more about visuals Follow Powerviz on LinkedIn Hierarchical Bar Chart New, exciting changes were made to Hierarchical Bar Chart. Bubble Matrix for stacks - The visual now provides the option to show stacks as bubbles below the bars. lues into bar-and-bubble matrix layouts for clearer comparison of category totals and component contributions. Display Stacks as Clustered Columns - From the format pane settings, you can choose to display stacks as clustered column chart. Filter using legends - You can click on a stack legend to filter out other stacks. On mouse hover relevant stack gets highlighted. Stack ribbons and %age of total - You can now choose to connect stacks with ribbon and show percentage of total for each stack. Resources Check out the Hierarchical Bar Chart Update YouTube video. Download the visual from APPSOURCE Download a sample pbix file For more information refer to Hierarchical Bar Chart or contact us. That’s a wrap! The European Microsoft Fabric + SQL Community Conference is just getting started, and there's never been a more exciting time to be part of the Power BI community. We hope these updates help you discover new capabilities, streamline your workflows, and get even more value from your data. Be sure to explore the links throughout this post for deeper technical guidance and follow along with the latest announcements and sessions from Barcelona.
Advanced NL2SQL and advanced DAX generation in Fabric data agents (Generally Available)
Data agents in Microsoft Fabric let users ask questions about data in plain language. As a creator, you connect an agent to supported sources—including SQL databases, lakehouses, Power BI semantic models, and eventhouses—add the business context it needs, and share it with users across Fabric, Teams, Microsoft 365 Copilot, Copilot Studio, and Microsoft Foundry. The data agent can then respond like a domain expert that understands what your tables mean, how your metrics are defined, and which source can answer a question. The usefulness of a data agent ultimately depends on a simple question: can it translate what a user asks into the right query? That is harder than it sounds. Enterprise schemas are rarely intuitive. Several tables, columns, measures, or join paths may appear to answer the same question, so a query can be technically valid but still return the wrong result. Advanced NL2SQL and advanced DAX generation help address this problem by allowing the agent to explore the schema and data, validate assumptions, and refine its approach before generating a query. Both capabilities are available today, and we'll begin rolling the core improvements out to the standard runtime on November 2, with the rollout expected to complete by November 10. In this post, we'll look at how they work, what our internal benchmarks show, the new configurations available for shaping how the agent behaves, and what to consider as you re-test and evaluate your data agents. Why conversational analytics gets harder with enterprise data Turning a plain-language question into a correct query is a complicated problem, both for SQL and DAX, requiring the agent to resolve several challenges at once: Schemas are large. Enterprise sources can contain hundreds of tables, columns, and measures, and the names alone may not explain when or how they should be used. Business context exists outside the data. Definitions such as “revenue” or “active customer” often live in documentation, previous queries, or analysts’ knowledge rather than in the source schema. Filter values must match the way data is stored. Users describe concepts naturally, but those concepts must map to the values in the data. For example, a user may ask about “California” when the relevant column stores the value “CA.” Questions are ambiguous. More than one column, calculation, or join may appear to satisfy a request. Individually, each of these can be managed with enough hand-written guidance. The difficulty is that they compound: a single-pass generator has to resolve all of them at once — your schema, your definitions, your filter values — committing to an interpretation before it has any evidence that interpretation is right. When it guesses wrong, the query still runs, and the answer looks no different from a correct one. Advanced NL2SQL and advanced DAX generation are built around that problem. Rather than generating a single pass, the agent gathers evidence first. Advanced NL2SQL: the agent explores before it writes The shift is architectural. Instead of one-shot generation, the agent has sub-tools to explore schema, test joins, and look up real filter values before it writes SQL. Under the hood, you can inspect each step the agent took, and the assumptions and reasoning behind it. It operates in three modes, each backed by its own sub-tools: Mode What it does Example query following Finds a relevant example query and answers by making approved modifications, such as literal filter or parameter substitutions. Clarifying questions Determines it can’t answer reliably without more information and asks targeted clarifying questions before generating SQL. Data exploration When no example directly applies, learns from existing example queries to infer business logic, explores the data to validate assumptions, and iteratively refines its approach. What you get as a creator: Advanced NL2SQL explores the SQL source schema, tests joins, and looks up real filter values before generating SQL, helping it choose the appropriate tables, columns, relationships, and filters. Schema descriptions and topics that teach the agent what your data means, so you get better answers with fewer instructions. Better example-following and filter-value substitution for novel questions. Handles large schemas far better and runs faster end to end, with more reasoning detail for debugging. New configurations: schema descriptions and topics Two new configurations give the agent more of the context it needs. Both are available for SQL data sources on the preview runtime. Schema descriptions Schema descriptions attach business context to the objects in your data source — what a table holds, what a column represents, and how each should be used. The agent uses them to interpret large or ambiguous schemas and generate a more accurate query. They matter most where object names don’t carry meaning on their own, or where several tables and columns look interchangeable. Figure: Schema descriptions add business context to schema objects, helping the data agent interpret your data. Topics Topics help you give the agent detailed guidance without sending every instruction with every question. The agent searches your topic content and uses only the sections relevant to the current request. Use data source instructions for rules that must always apply, and use topics for deeper guidance that applies only to a specific subject. You can write topics in Markdown, import multiple files, and review the run steps to see which topics the agent selected. Figure: Topics organize subject-specific instructions in Markdown to guide the data agent’s query generation. Internal benchmark testing: Advanced NL2SQL on Preview Runtime vs NL2SQL on Standard Runtime Better reasoning matters only if it produces better answers without making users wait substantially longer. In an internal benchmark set evaluating answer quality and end-to-end latency, the preview runtime delivered the stronger outcome, answering more questions correctly than the standard runtime while reducing both median and tail latency. Smart-answer accuracy increased from 78.8% to 90.9%. Latency improved at the same time: P50: 28.4 seconds to 26.0 seconds P95: 46.8 seconds to 32.9 seconds Figure: Smart answer accuracy and end-to-end latency, standard runtime versus the Advanced NL2SQL preview, on an internal benchmark set. Accuracy rises 12.1 points to 90.9%, while P50 latency falls 8.3% and P95 falls 29.8%. *Performance results are based on Microsoft internal testing conducted in 2026 using an internal evaluation set comparing standard NL2SQL to the advanced NL2SQL preview in Microsoft Fabric. Actual results may vary depending on workload, data model, and configuration. The P95 result shows the largest latency improvement in this benchmark, with latency decreasing from 46.8 seconds to 32.9 seconds. This improvement is most relevant to the slower, more complex queries represented at the tail of the distribution. Advanced DAX generation: agentic reasoning over semantic models The agent calls tools, inspects results, and refines its approach to answer harder questions. Instance value indexing searches real column values, so filters match your data. And you get consistent results across Data Agent, Power BI, and M365 Copilot. For users, this means the agent is more likely to resolve filter terms to the values stored in the semantic model and return consistent answers across supported Microsoft experiences. Internal benchmark testing: Advanced DAX generation improves accuracy while cutting latency Our internal Advanced NL2DAX evaluation showed improvements in both answer accuracy and latency. Smart-answer accuracy increased from 89.15% to 91.86%. At the same time: P50 latency: 39.1 seconds to 20.4 seconds P95 latency: 97.5 seconds to 34.7 seconds This represents an approximately 48% reduction in P50 latency and a 64% reduction in P95 latency, along with a 2.7 percentage-point increase in smart-answer accuracy. *Performance results are based on Microsoft internal testing conducted in 2026 using an internal DAX generation evaluation set comparing the baseline to the advanced DAX generation preview in Microsoft Fabric. Actual results may vary depending on workload, semantic model, and configuration. Rolling out the runtime improvements We'll begin rolling the core Advanced NL2SQL and advanced DAX generation improvements out to the standard runtime on November 2, with the rollout expected to complete by November 10. As part of this rollout, the standard runtime will also move to GPT-5.6 Terra. As the rollout reaches your environment, data agents using the standard runtime will receive these improvements and the model upgrade by default, with no opt-in required. What is moving to the standard runtime: Advanced NL2SQL: improved example-query following, filter-value substitution, and data exploration. Advanced DAX generation: the agentic tool-calling loop and instance value indexing. Model upgrade: The standard runtime will move to GPT-5.6 Terra for orchestration and supported data agent capabilities. What stays in preview: Schema descriptions and topics for SQL sources. We believe these changes deliver significantly better results, based on improvements observed in our internal evaluations. However, results can vary by data source, model, instructions, examples, and workload. Creators can re-test and evaluate their agents on the standard runtime between November 2 and November 10 as the rollout reaches their environment, or try the changes now on the preview runtime. Moving from one-shot generation to an agent that explores data can change how existing instructions and examples are interpreted. We recommend re-testing: Run your existing question set against the standard runtime as the rollout reaches your environment between November 2 and November 10, or switch to the preview runtime to try the changes now. Rerun your evaluations with the Fabric data agent SDK and a ground-truth set. Compare the new results with your previous baseline, and use the visible reasoning steps to identify where the agent’s assumptions differ. Tune your example queries and instructions based on what the evaluation shows. The agent now explores your data and adapts examples through substitution, so guidance written to work around the previous behavior may no longer be needed. Re-testing and evaluating your agents will help you confirm the expected quality improvements for your own data and scenarios. Next steps If you build data agents in Microsoft Fabric, re-test your agents on the standard runtime as the rollout reaches your environment between November 2 and November 10, or try the changes now on the preview runtime. For more details: Fabric data agent runtime: Learn what the standard and preview runtimes each contain, how to switch between them, and what’s in the preview runtime today. SQL sources in Fabric data agent: Learn how NL2SQL and Advanced NL2SQL generate queries over Lakehouse, Data Warehouse, SQL Database and Mirrored Database sources, and which configurations each one can use. Semantic model best practices for data agent: Semantic model best practices for data agent: Learn about advanced DAX generation and how to prepare a semantic model so the agent can answer questions accurately.Introducing Rayfin: A new AI-first way to build, deploy, and govern application backends
The way organizations are building apps is changing. AI and coding agents have made it easier than ever to turn an idea into an app. But keeping those applications secure, compliant, and governed has only gotten harder.
Unlocking LLM-Powered through Data Agent from your Mirrored Databases in Microsoft Fabric
Fabric Data Agent now support mirrored databases--making it easier than ever to put your operational data to work with AI. As organizations depend on an ever-growing mix of data sources, the ability to seamlessly integrate and analyze data—no matter where it resides—is critical. This is where mirroring in Fabric comes in. With open mirroring, you can bring data from Independent Software Vendors (ISVs) directly into Fabric’s Delta Parquet format, ensuring it stays automatically in sync with the source. Now, with mirrored database artifacts connected directly to data agents in Fabric, you can unlock the power of Large Language Models (LLMs) to query, reason over, and generate insights from your operational data--accelerating time-to-insight across your entire data estate. What Are Mirrored Database Artifacts? Mirrored database artifacts are representations of your external databases—such as Azure Cosmos DB, Azure SQL Database, Oracle, Snowflake, Databricks—mirrored into Microsoft Fabric. This enables you to leverage Fabric’s AI-driven analytics and LLM capabilities over data that previously lived outside the Fabric ecosystem. Supported Mirrored Artifacts in Data Agent include: Mirrored Azure Cosmos DB Mirrored Azure Database for PostgreSQL Mirrored Azure Databricks catalog Mirrored Azure SQL Database Mirrored Azure SQL Managed Instance Mirrored Oracle Mirrored Snowflake Mirrored SQL Server Database With the latest release, users can add mirrored artifacts directly to Fabric Data Agent. Users no longer need to create an intermediary Lakehouse to bridge this gap. Once connected, the data agent leverages its NL2SQL (Natural Language to SQL) capability to generate and execute SQL queries against the SQL Analytics Endpoint built into the mirrored artifact. This means users can ask natural language questions and get instant, actionable insights—powered by LLMs—across all your mirrored data sources. Figure 1: The user has a data agent loaded with a mirrored databricks catalog and a mirrored oracle database. The user hits on add datasource and filters by mirrored items in Fabric. The user then selects a mirrored cosmos db and adds it to the data agent. Key Features and Benefits By connecting mirrored artifacts directly--without extra steps--you can unlock instant insights, streamline workflows, and power smarter applications across your data estate. Direct Integration: Add mirrored artifacts as data sources in Fabric data agents without the need for intermediate Lakehouses or shortcuts. AI-Driven Analytics: Use data agent’s built-in NL2SQL to query your data in plain English and receive insights instantly. Broad Compatibility: Integrate data from Azure, Oracle, Snowflake, Databricks, and more. Customizable Knowledge: Curate the data agent with instructions, data source instructions, and few-shot queries for all mirrored data sources. Scoped Context: Select relevant tables to focus the data agent’s knowledge and responses. Seamless AI Application: Bridge the gap between your external data and AI applications in Microsoft Fabric. Getting Started Mirror Your Database: Use mirroring for Azure databases, or open mirroring to bring external databases into Microsoft Fabric as a mirrored artifact. Add to Data Agent: In the data agent interface, add the mirrored artifact as a data source. Curate and Query: Customize your data agent with instructions and examples, then start asking questions in natural language to generate LLM-powered insights. What’s Next? Microsoft Fabric is continuing to expand support for additional data sources. Coming soon, you’ll be able to connect SQL Database artifacts. To learn more about data agent, check out the Fabric data agent concepts (preview) documentation. For more details on how to mirror your databases into Fabric, review the Open Mirroring documentation. Summary By connecting mirrored database artifacts directly to Fabric Data Agent, you can unlock the full power of LLM-driven analytics across all your data—no matter where it lives. This new capability makes it easier than ever to bridge the gap between your data and AI, empowering you to make smarter, faster decisions.FabCon/SQLCon Barcelona 2026: What’s new in Fabric Data Factory
If you haven't already, check out FabCon and SQLCon 2026 in Barcelona: Building the data foundation for Microsoft Copilot and Agents for a complete look at all of our FabCon Europe announcements across Fabric and Microsoft Databases. Almost every conversation I have with customers comes back to the same issue: data fragmentation remains the single biggest challenge to harnessing AI. Harmonizing data across the systems and applications where it resides, and making that data available to the people, applications, and agents that need it, is the work — and most teams are still doing a surprising amount of it by hand. At the European Microsoft Fabric and SQL Community Conference, we are showcasing what’s possible when a data platform is built to address fragmentation across silos while respecting the diversity and heterogeneity of enterprise data estates. We’re introducing more than 45 new capabilities, including additional sources for unifying and distributing data, stronger replication governance, new data destinations, more resilient pipelines, richer monitoring, and AI assistance across the end-to-end experience. The following capabilities, announced at FabCon Barcelona, can help teams operate their multicloud data estates day in and day out. A foundation for the AI-powered enterprise AI is redefining how every organization operates. But AI is only as powerful as the data that grounds it. That is why data integration has never been more important. At Microsoft, we are building the foundation for the AI-powered enterprise with Microsoft Fabric and Data Factory, unifying data across operational systems, clouds, applications, and analytics platforms into a single, trusted, governed data estate. From zero-ETL data unification and real-time intelligence to metadata-driven integration and agentic experiences, we are enabling organizations to turn fragmented data into enterprise intelligence that powers business decisions, workflows, and AI agents. Our ambition is not simply to modernize data integration, but to transform it from a process of moving data into the intelligence layer that powers AI agents, applications, and people with trusted business context. Unifying the data estate: Zero ETL, and now zero excuses Data silos create friction that makes it harder to realize value from data. Data must be copied or extracted, the pipelines that move it must be managed and monitored, and data distributed across systems must be governed. Mirroring helps eliminate this friction by continuously bringing data into OneLake without placing a load on source systems or requiring pipelines to maintain. It has become a fast way for customers to obtain a governed, current view of their operational data. Today, we are extending Mirroring in the two directions customers requested: adding more sources and increasing each source by going beyond data replication. Mirroring for Google BigQuery and Mirroring for SharePoint Lists are both generally available, opening two of the most requested paths into OneLake — one at analytical scale, the other for the lists business teams keeps their working data in. Coming soon, Mirroring for Dynamics 365 Business Central will let customers bring data from selected tables and companies directly into OneLake, unlocking richer reporting and analytics across Microsoft Fabric. Extended Capabilities in Mirroring are now generally available, including Change Data Feeds. We’re also announcing an addition to the extended capabilities in Mirroring for Snowflake: replication of security policies. Governance has traditionally been a challenge for replication. The data may be copied cleanly, but its permissions are not requiring customers to rebuild security policies manually in the destination. Replicating Snowflake security roles in OneLake carries that governance context with the data. Open Mirroring continues to grow through our partner ecosystem, with additional partner investments landing this release. This is an important dimension of our Mirroring roadmap and the only way in which we can cover the breadth of sources that matter to every organization. We are always grateful to our partners for joining us in building a complete and comprehensive Mirroring roadmap. Learn more about extended capabilities in the Mirroring documentation. Multicloud, petabyte-scale data distribution Getting data in is only half of the story. Increasingly, our customers need to distribute data to another cloud, a partner, or their own customers' environments. Bespoke approaches to data distribution are costly and can impose a large integration tax. With Data Factory, our vision is to support data movement across clouds and diverse data platforms. We have a breadth of capabilities in our portfolio to drive data movement from batch to streaming scenarios and multicloud sources and destinations. Copy job in Data Factory is purpose-built for data distribution, with capabilities such as Change Data Capture, Slowly Changing Dimension Type 2, and audit capabilities that preserve history and lineage in the destination instead of requiring them to be reconstructed later. An exciting addition to the Copy Job is the integration of batch-to-stream and stream-to-batch capability, minimizing the gap between these two modalities in data integration. Copy job can now write changes directly to eventstreams, bringing the wealth of batch data sources to enable greater flexibility. Copy job execution can now also be triggered through Fabric Activator, which means that Copy jobs can be triggered in more ways than through a wall-clock schedule. Both of these improvements enable customers to build richer and more elegant architectures for how data integration is done. We are also expanding Copy job’s multicloud data distribution capabilities, giving customers more flexibility in where data lands and greater control over how delivery is managed. This includes SharePoint folders as a destination and support for loading multiple source folders into separate destination tables within a single job. To make distribution easier to operate, audit columns add traceability to data delivered to Snowflake, while workspace monitoring brings job health and throughput into a centralized view so teams can spot failed or slow-running jobs without opening each one. Custom staging database and schema support give customers control over where CDC and SCD Type 2 staging objects are created, helping data delivery fit their existing security and operational practices. Transformation in the tools people already use Transformation is the most widely used integration workload in the Microsoft Fabric ecosystem because it serves people across the full spectrum of data work. A citizen developer shaping data in Power Query, a data engineer using Notebooks to clean, join, and enrich data with custom code, and a code-first team managing a dbt project are solving the same fundamental problem through very different tools and experiences. Our goal is to serve each of them in the way they work best on a shared foundation that does not compromise scale, performance, or governance. At FabCon, we are advancing that vision across Power Query, Dataflow Gen2, notebooks, and dbt — making transformation faster to run, easier to reuse and operate, and more consistent across Power BI and Fabric. In Dataflow Gen2, we are improving performance and scale with optimized Lakehouse writes, V-Order compression controls that help teams balance write cost with downstream query performance, and a modern evaluator expanding across connectors for more consistent execution. We are also introducing Shared Queries so teams can define preparation logic once and reuse it across dataflows, reducing duplication and keeping business rules consistent. We are broadening where Dataflow Gen2 can deliver prepared data by adding destinations across Fabric, Snowflake, and SharePoint. Other multicloud destinations, including Amazon S3 and Google BigQuery, are coming soon. The additions I find particularly compelling are Excel and email destinations: they put trusted data directly into the business tools and workflows where decisions happen, reducing the manual exports and handoffs that still sit at the end of too many data processes. We are also making Dataflow operations easier to manage. Downloadable diagnostics and Dataflow Gen2 run details in Monitoring Hub give teams the execution context they need to move from a failed refresh to an actionable cause faster. One of the innovations I am most excited about is bringing years of Power Query Online advancements to Power BI Desktop. This creates a more consistent Power Query experience across products and gives authors a more natural path from self-service preparation in Power BI to governed, scalable transformation in Dataflow Gen2. For our existing Power BI customers, we are also enhancing the Dataflow Gen1-to-Gen2 Migration Wizard to provide a guided, lower-risk path to modernization. At FabCon, we are adding API support to automate migration across larger estates, a downloadable upgrade report to capture readiness and follow-up actions, and new validation rules to surface compatibility issues earlier. Together, these enhancements help administrators plan and track the move to Fabric with greater confidence. For code-first teams, we are making dbt Job in Fabric generally available with broader enterprise support while preserving the familiar dbt workflow. We are landing dbt-generated logs in OneLake so run history, model execution time, and test failures can be analyzed; extending where teams can build models through Lakehouse and additional Fabric adapters; and carrying environment configuration through deployment pipelines with variable library support. Support for dbt Core v2, previously known as Fusion, is coming soon. Orchestrating data with ease: From low code to pro-code Scale, reliability, and flexibility are essential to any credible data integration platform. Pipelines in Fabric Data Factory power more than 25 billion orchestration runs each month. Built on Azure Data Factory’s proven platform, pipelines provide a widely used orchestration foundation for enterprise data integration. At FabCon Europe in Barcelona, we’re delivering the next wave of orchestration capabilities in Fabric Data Factory: more resilient pipelines, richer monitoring, agentic experiences that reason over pipeline runs, new Actions activities that enable pipelines to integrate directly with Fabric platform APIs, and business activities. The Lakehouse Maintenance activity enables you to run vacuum and optimize directly from your pipelines, so maintenance happens on the same cadence as the data that needs it — faster queries and lower storage overhead, with no separate job to own. Pipeline activities also support exponential retry, where retry delays increase over time, so transient throttling resolves itself instead of turning into a failed run and a morning of manual re-runs. Outlook activity now supports service principal and workspace identity, so notifications run on a workspace identity or service principal rather than a personal account — email workflows remain operational when personnel change and can be deployed cleanly across environments. Pipeline-level dependencies let you define relationships between pipelines, enabling dependent pipelines to run only after upstream pipelines succeed. Monitoring itself gets richer: runtime events, performance metrics, and intelligent error classifications now sit together in Monitoring Hub, so an engineer goes from alert to cause without leaving the monitoring surface. Agentic investigation uses AI-powered analysis to help identify whether an issue may be related to code, data, or capacity. New activities extend what a pipeline can do. We’re introducing activities that let you invoke Fabric platform APIs natively from pipelines and incorporate business activities into pipeline workflows. Agentic approvals with the Approval activity put human-in-the-loop decisions inside the pipeline, with an agent that evaluates signals and surfaces a risk score based on the environment, data classification, volume, and requestor, and leaves a full audit trail on every decision. AI-assisted data integration AI in data tools has traditionally taken the form of a chat window used primarily for authoring and starting conversations. What we’re delivering now is different: AI-assisted and AI-driven experiences integrated across the data integration lifecycle, from authoring and operationalizing to monitoring, troubleshooting, repairing, and automating workflows. Fabric Model Context Protocol (MCP) and Fabric Skills provide the foundation for this end-to-end continuum. The MCP gives agents a standardized way to reach tools and resources, which means a developer can work with an agent to create a dataflow, write and update queries, trigger a refresh, start a pipeline run, and check its status — without a bespoke integration for each one. Agent-driven automation scenarios build on this foundation, and the Data Factory MCP and related skills are publicly available for developers. Unified Copilot and Fabric Skills investments give the assistants integrated across different surfaces of Fabric a common understanding of the estate and shared context. AI-powered operations and observability-focused agents turn a wall of run history into an actionable answer about what broke. Taken together, they shorten the two tasks that consume most of a data engineer's week: writing the logic and finding out why it stopped working. Upgrading the integration estate you already have Azure Data Factory has earned the trust of customers worldwide by powering mission-critical data integration workloads over the last 10 years. Organizations that rely on Azure Data Factory and Azure Synapse pipelines have built critical data integration foundations that form the backbone of their business operations. Fabric Data Factory builds a trusted foundation while bringing customers into a unified analytics platform designed for the AI era. With OneLake, Copilot-powered experiences, integrated analytics, and Fabric-native innovation, customers can modernize their data integration estate while preserving the investments that matter most. At Fabric Conference Barcelona, we are excited to announce a streamlined upgrade experience that helps Azure Data Factory customers begin their journey to Fabric Data Factory with confidence. This new experience enables Azure Data Factory customers to bring their existing data factory into Microsoft Fabric, view their Azure Data Factory pipelines directly within Fabric, and continue using familiar management and monitoring experiences, all before making any migration decisions. Customers can experience Fabric immediately while preserving their existing Azure Data Factory investments. This is another important step toward our vision that every Azure Data Factory customer can become a Fabric Data Factory customer. Learn more about upgrading from Azure Synapse and Azure Data Factory to Microsoft Fabric. Customers leading the way The clearest way to explain what we are building is to watch a real customer use these capabilities for mission critical use. London Stock Exchange Group (LSEG) operates some of the world's most critical financial market infrastructure, and its data and analytics business turns operational data from many systems into governed data products that customers consume inside their own environments. Before Fabric, each of those three steps had its own machinery: pipelines to build and watch, analysis running on copies that engineers spent time reconciling, and a bespoke last mile for every customer of LSEG that was onboarded. LSEG’s data integration architecture is now vastly modernized. Mirroring brings the operational data into OneLake and keeps it current, the Fabric workloads shape it into the products customers pay for, and Copy job distributes those products out to customer environments — whether that environment is Fabric, another cloud such as Databricks or Snowflake, or a platform of their own. As Toni McDerment, who leads data interoperability at LSEG, puts it, the team is expanding distribution while streamlining integration. That is the whole data integration story in one customer: ingestion, analytics and delivery on a single estate. Across the conference, other customers showed the same shift from their own angles: Mediterranean Shipping Company — the world's largest container shipping company, with more than 1,000 vessels — replaced six-hour (sometimes multi-day) ETL cycles with real-time mirroring from SQL Server 2025 into Microsoft Fabric, moving operational data into analytics in seconds instead of hours while cutting storage costs 20% through new compression. For KPMG, onboarding clients and provisioning environments for new engagements — which previously required tickets and setup across multiple tools — decreased 87% from about 16 hours to 2 hours. Porsche Cup Brasil replaced spreadsheet-based, manual race reporting with a unified Fabric data pipeline — streaming more than 180 telemetry channels from 75 race cars via Eventstream into Eventhouse, Azure Data Factory, OneLake, and Power BI. The solution reduced engine-fault detection and response time from approximately 30 minutes to minutes, allowing teams to remove a failing engine from the grid before a breakdown. And we have many more customers achieving similar outcomes, with the same conclusion: the ceiling on what AI can do for a business is set by the quality, accessibility, and governance of the data underneath it. Looking ahead Data Integration is always a means to an end – data movement, orchestration, connectors are all structural elements that contribute toward the goal of turning massive amounts of data into business value. With Microsoft Fabric, our goal is to make these constructs disappear so that customers can focus on what ultimately matters – harnessing the value that resides in the data to improve business outcomes by leveraging AI. Our core focus in Data integration is making our products enterprise ready, work across clouds and be AI-driven. The work across our key investments and the 50-plus capabilities in this release are driving towards it. We are excited about what comes next and grateful to the customers, partners, and our community members who build this with us. Your ideas shaped this release, and they are already shaping the next one. Read the full list of FabCon announcements in the September release blog. Learn more Data Factory in Microsoft Fabric Get started with Microsoft Fabric What's new and planned for Data Factory In-depth technical blogs on the Microsoft Fabric Updates BlogPower BI August 2026 Feature Summary
Welcome to the August 2026 Power BI Feature Summary! This month includes updates across reporting, modeling, data connectivity, mobile, embedded analytics, and developer experiences, with a mix of generally available enhancements and new preview capabilities. Let's take a look at what's new. Download Power BI Desktop Events and Announcements September 15 | What a Winning Power BI Dataviz Looks Like Meet the finalists headed to Barcelona to compete for the title of Power BI Dataviz World Champion. Explore the visuals that got them there, hear their tips for creating standout dataviz, and join their virtual cheer squad before they face the ultimate challenge: 20 minutes to create a championship-winning visual live on stage. Reserve your spot. Community Conference Tickets are Getting Low The European Microsoft Fabric and SQL Community Conference take place September 28 to October 1 in Barcelona, Spain, bringing together Microsoft experts and community leaders for exclusive content, live demos, and key announcements, along with more than 130 sessions spanning Fabric, Azure AI, Databases, Power BI, and Microsoft Purview. And of course, the live Dataviz World Championships finale! The most recent event in Atlanta sold out. Don’t miss out and register for the Barcelona event with code FABCMTY200 to save €200. General Deprecation of Old File Picker experience in Power BI Desktop In April we deprecated the old file picker experience in Power BI Desktop. Starting in October, users still utilize versions of Power BI desktop from March 2026 or earlier will no longer be able to save and share Power BI files to One Drive and SharePoint. Please ensure you update Power BI desktop to keep using this functionality. Note: No action is required from users as part of this deprecation; this is simply an informational announcement. Copilot and AI Updates to required semantic model permissions for Fabric Apps Currently, users need Build permission on the underlying semantic model to view and interact with a Fabric App created with the data app template. In early September, consumers will need only Read permission on the semantic model, along with access to the Fabric app. This change supports least-privilege of access by no longer requiring the broader Build permission for app consumption. App authors will still require Build permissions on the semantic model. Learn more about creating a Fabric app connected to a semantic model. Copilot Summary and Copilot Narrative can now read visuals hidden behind bookmarks Authors commonly use report bookmarks as tabs to organize content by showing and hiding visuals on a page. In these designs, certain visuals are hidden by default and revealed only when a display-only report bookmark is triggered. Copilot previously skipped these visuals entirely, so its Summary responses reflected only part of the report. Copilot Summary can now read and consider these bookmark-revealable visuals in its responses. This covers only visuals that are hidden by default and made visible by a display-only report bookmark triggered through a bookmark button or bookmark navigator. Authors can also select these hidden visuals when configuring the Copilot Narrative visual. Visuals are read in place with no changes to the bookmark state, and RLS and OLS permissions stay fully enforced. To learn more, refer to Summarize a report with Copilot and Create a narrative visual with Copilot for Power BI. Reporting Modern visual defaults and customize themes formatting panes (Generally Available) New reports created in Power BI Desktop and the Power BI service start with the Fluent 2 base theme, giving you a polished, consistent look without formatting each visual. Use the Theme pane to set the base theme, adjust colors to your brand or pick from one of the available color palettes, change text styles, set specific visual properties, page settings, and the filter pane look and feel across your entire report, and import or export custom theme, in both Desktop or web. Figure: Customizing a report’s base theme and color palette in the Power BI Theme pane. In this release, we also removed all font overrides from the base theme, so the Text section in the Theme pane now works as intended. Change a font in one place to update titles, callouts, labels, and general text across every visual in your report, including new visuals added later. For a deeper look at what's included, see the Deep dive into Modern Visual Defaults and Customizing Theme Improvements and Deep dive into modern visual defaults and formatting your entire report blog posts. Learn more about the base themes and the Theme pane in the Visual defaults in Power BI reports documentation. Date picker for Slicer visual (Generally Available) Date pickers combine relative selections that roll forward as time passes with a calendar and slider for manual date range or single date selection. You can anchor relative options to today, the first date, or the last date in your column. Figure: Enabling single-date selection for the Date picker slicer in the Selection controls settings. New in this release, the Single date setting under Visual > Slicer settings > Selection controls restrict the slicer to one date at a time—the calendar and slider allow only a single date, and the relative options are limited to selections that return a single date. And you can now clear selections from a header icon in addition to slicer header. For more information, see the Slicer visual in Power BI documentation. Center value for donut chart (Generally Available) Donut charts can now show value in the center, no overlaid card required. Enable the center value and the donut displays it right in the middle. You control the details in the formatting pane: value format, display units, and font. Add an optional label above or below the value, place an image above or below it, or change the background filling or image of the center. Need something other than the total? Add a custom measure to the center value in the formatting pane. Figure: A donut center can show a plain total or a custom value, label, image, or background to match your report. The center value stays in sync with your data: filtering, cross-highlighting, drill-down, and selecting a slice all update it automatically. And because it lives inside the visual, it stays aligned across resizing, mobile, and dashboards, and works with screen readers. To add a center value, select your donut chart, open the formatting pane, and turn on Center value. To learn more, refer to Add a center value to a donut chart. Comments support for reports in org apps Users can now collaborate on reports in org apps using comments. Comment on report pages and visuals, start discussions with colleagues, and use ‘@mentions’ to notify others and bring them into the conversation. Comments also capture the current report context, helping others understand exactly what you're seeing when providing feedback or discussing insights. Figure: Comments pane open on a report in an org app comment box highlighted. To learn more about, see comments in Power BI reports. Matrix: Expand and collapse for column headers (Generally Available) You can now expand and collapse column headers in the matrix visual, just like you already can for row headers. When your matrix has more than one field in the Columns field well, +/- icons appear on the column headers so you can combine groups or reveal more detail directly in the visual. You can also customize the color and size of the +/- icons under Column headers > +/- icons in the format pane. Figure: Customizing the color and size of expand and collapse icons for matrix column and row headers. To learn more, see Expand and collapse row and column headers. Matrix: Set the default freeze state for row headers in the format pane Report authors can now set the default freeze state for matrix row headers directly in the format pane. Turn the Row headers toggle on or off under Layout > Freeze to control whether row headers stay visible or scroll away when consumers scroll horizontally. This default is saved with the report and applies to everyone who views it. Figure: Setting the default freeze state for matrix row headers in the Visual formatting pane. This new setting is especially useful for mobile layouts. On phones, portrait orientation limits horizontal space, so you might want to unfreeze the row headers on your mobile layout to give data values more room, while keeping them frozen on the desktop layout, which typically uses landscape orientation. Figure: Setting the default freeze state for matrix row headers in a mobile layout. The right-click Freeze row headers and Unfreeze row headers options are still available and, as before, remain transient — they apply only to the current viewing session, so consumers can flip the state temporarily without changing the report author's default. To learn more, see Freeze row headers. OneLake file URLS for report visuals and maps (Generally Available) You can now use links to image files stored in OneLake as an image source anywhere where Power BI accepts an image URL. This works in the image visual, table and matrix cells, the card visual, button and list slicers, as well as for custom icons in the Icons cell element when you use the Field value format style. OneLake file links work even though they require authentication—Power BI signs in on your behalf when the report loads, so you don't have to make images anonymously accessible to use them in your report. Copy the file URL from OneLake and paste it into any image URL field or into a column or measure set as Image URL. Example: https://onelake.dfs.fabric.microsoft.com/{workspace-id}/{item-id}/Files/{path-to-image Figure: Using a OneLake file URL as the image source for an image visual in Power BI. Reference layer maps and shape map files can also be linked from OneLake file URLs. Keep your files in OneLake and use them in your Power BI reports today! For more information, see the Display images in a table, matrix, or slicer and Image visual in Power BI documentation. Outer padding for bar, column, line, ribbon, and waterfall charts in formatting pane (Generally Available) Column, bar, clustered, line, ribbon, and waterfall charts now include an Outer padding setting under Layout, giving you control over the space between the edges of the plot area and the first and last categories. This is helpful when you want your chart to fill the visual—especially in scenarios where you've turned off the category axis and are relying on a legend, like a clustered column chart used as a comparison view. Then, you can simply resize the visual to get the look you want on your report page. Reducing outer padding on line charts with categories also lets you show more data before a scrollbar appears. Figure: Adjusting outer padding and spacing between categories to control how charts fill the plot area. To use it, select the visual and, in the Format pane, expand Columns, Bars, or Lines > Layout, then set Outer padding to 0%. To fully fill the plot area, also set Space between categories to 0%. For more information, see the Customize x-axis and y-axis properties documentation. Azure map reference layer shape-matching improvements (Generally Available) Shape matching in Azure Maps reference layers now delivers significantly better performance. When you match your Power BI data to shapes in a reference layer—for example, joining sales data to a set of custom territories or regions in a GeoJSON file—reports now render faster, and interactions like filtering, cross-highlighting, and zoom respond more smoothly. No configuration change is required; existing reports automatically benefit from the improvements. Figure: Improved Azure Maps reference layer performance, with faster rendering shown in the Performance analyzer. For more information, see the Add a reference layer to the Azure Maps Power BI visual documentation. Azure map loads filtered selection and autozoom (Generally Available) The Azure Map visual has a 30,000 data point rendering limit, and previously, points beyond that limit didn't display—even when you filtered the report to focus on a smaller area whose data hadn't originally loaded. Now, when you filter to a smaller selection, the visual reloads to include the newly relevant data points that weren't part of the original 30,000. This means you can start with a broad view of your data and drill into any subset—by region, category, time period, or through cross-filtering—and be confident the map shows the complete picture for what you've filtered to, not just what fit in the initial load. Figure: Filtering the report to Washington reloads the relevant ZIP code shapes and automatically zooms the Azure Maps visual to the selected area. The Azure Map visual also now automatically zooms to fit the currently filtered area. When you or a report viewer applies filters, use slicers, or cross-filter from another visual, the map reframes to the extent of the visible data instead of staying at the original viewport. This keeps the map focused on the data being analyzed and removes the need to manually pan and zoom after every filter change. For more information on Azure Map, see the Azure Maps visual for Power BI documentation. Slicer visual dropdown border color, open icon, and hierarchy expand and collapse icon colors in formatting pane (Generally Available) Based on your feedback, we’ve added more slicer formatting options beyond the selection icon, giving you greater control over the slicer’s appearance. When you add more than one field to the slicer to have a hierarchy in vertical list or dropdown style, make sure the icon color matches your report. And dropdown style, offering a compact slicer to your report has more formatting options. Figure: Customizing the slicer dropdown border, open icon, and accent bar in the Visual formatting pane. The new top-level Dropdown section in the Visual formatting pane lets you customize the dropdown box in both its open and closed states, including border color (with conditional formatting), rounded corners, and the open icon color (with conditional formatting) and transparency. A new Accent bar option adds a color bar to the dropdown box with settings for color (with conditional formatting), position, transparency, and width. For hierarchy slicers, the top-level Hierarchy section lets you color the expand and collapse icons (with conditional formatting) and choose which icon to use. For more information, see the Slicer visual in Power BI documentation. Modeling More control over Semantic Model refresh in Power BI Service We've added new refresh options in Power BI Service to give you greater control over how semantic models are refreshed. Previously, selecting Refresh always performed a schema sync followed by a data refresh. While this works well in most scenarios, there are times when you may want to refresh data without updating the model schema or vice versa. For example, in Direct Lake semantic models, a Lakehouse table might have changed by adding new columns, but you may want to load the latest data without bringing those schema changes into the model. Figure: New model refresh options in Power BI Service Model view. The Refresh button now includes three options: Refresh schema and data: Updates the schema and then refreshes data. Sync schema only: Applies schema changes from the data source, such as new columns or data type changes. Refresh data only: Loads the latest data while preserving the current semantic model schema. In addition, refresh operations can now be performed at the table level. This allows you to refresh a specific table instead of the entire semantic model and choose whether to refresh its schema, data, or both. Figure: New table refresh options in Power BI Service Model explorer. These enhancements provide more flexibility and control over semantic model maintenance, helping you refresh exactly what you need while minimizing unnecessary changes and processing. Refer to the Data refresh in Power BI documentation for more information. Convert Direct Lake Tables to Import Storage Mode in Web Modeling (Preview) Direct Lake on OneLake supports composite models by allowing for mixing Direct Lake tables with Import and DirectQuery tables. You can now convert individual Direct Lake tables to Import storage mode directly in Power BI web modeling, giving you a seamless way to build a Direct Lake composite (mixed mode) semantic model. Open a Direct Lake on OneLake semantic model in web modeling, select one or multiple tables, and change the Storage mode from Direct Lake to Import in the Properties pane. The modeling flow guides you through configuring a connection and refreshing the table into Import mode. Because Direct Lake and Import tables support regular relationships, performance remains unchanged. Figure: Change the Storage mode property from Direct Lake to Import to convert a single table. A warning dialog will be surfaced before continuing. This feature will be rolling out over the next few weeks. Refer to the Direct Lake in web modeling documentation for more information. Direct Lake Calculated Columns (Preview) You can define calculated columns directly in your semantic model using DAX in web modeling and Power BI Desktop, without changing the table storage mode or modifying data upstream. Key behaviors Only the User Context expression context is supported. User-context-aware DAX functions such as USERCULTURE() are supported. Columns do not materialize and are evaluated at query time. Security context is respected (RLS and OLS). Columns cannot be used in relationships because they do not materialize. Expressions that do not reference user-aware functions or secured columns behave like any other DAX calculated column. Figure: Creating a concatenated location field. Figure: Month names displayed using USERCULTURE(). "Customer Location" returns the same value for every user unless the security context differs. "Month Name" adapts to the language of the person viewing the report. Figure: Month names are localized based on the user's culture using USERCULTURE(). No preview switch is required - open a Direct Lake on OneLake semantic model in the web or edit in Desktop, add a calculated column, and start authoring in DAX. To learn more, see the Create calculated columns in Power BI Desktop documentation. Data connectivity Accessibility, Dark Mode, and Usability Upgrades Theming improvements for better accessibility In this release, we’ve invested in a range of theming improvements that address color contrast and readability across both the light and dark themes. Users who rely on higher-contrast visuals will notice cleaner, more legible interface elements throughout the Power Query UI. Highlights: Improved color contrast across interface elements in both light and dark themes. More consistent theming that keeps controls, text, and icons clearly distinguishable. A stronger foundation for accessibility that benefits all users, regardless of theme preference. Figure: Better contrast for connectors and white background for icons. Better contrast for connector icons in Dark Mode Connector icons are how you quickly recognize your data sources, but in Dark Mode, icons with transparent backgrounds could blend into the surrounding UI and become hard to identify. We’ve fixed that. Now, when a connector icon has a transparent background, it automatically receives a white background in Dark Mode. The result: connector icons stand out clearly and stay easy to identify across the new Power Query UI, making it faster to find and select the right data source briefly. To learn more, refer to the Get data in Power BI Desktop documentation. Mobile Rotate view in the mobile report footer The Rotate view button is now available in the Power BI Mobile report footer, letting you switch layouts with one tap for larger visuals and a wider view—even when your phone is locked in portrait orientation. This is especially helpful for reports that don't have a mobile-optimized layout, which open in portrait by default. Just tap Rotate view to get a wider, more readable view, then tap again to switch back. This feature is rolling out soon to Power BI Mobile for iOS and Android - stay tuned! Figure: Tapping Rotate view in the report footer to get a larger view of the report. To learn more about the actions in the report footer, refer to the Explore reports in the Power BI mobile apps documentation. To learn how to build a mobile-optimized layout for your reports, see the mobile layout documentation. Export data to Excel in the Power BI mobile app Export data from Power BI report visuals directly from your phone or tablet. With Export data now available in the Power BI mobile app for iOS and Android, you can quickly take the data behind a visual into Excel for further analysis or sharing, without switching to the Power BI service on another device. To export, open a report visual in focus mode or open the visual’s More options (…) menu, and select Export data. The export starts automatically using the supported Excel format for that visual. The exported data reflects your current filters, slicers, drill state, permissions, and row-level security. When the file is ready, use your device’s sharing options to open, save, or share it. Figure: Exporting data from a report visual to Excel in the Power BI mobile app, via focus mode or open the visual’s More options (…) menu. To learn more, see Export data from a Power BI visualization. Embedded Analytics Enhancements to Power BI Embedding in SharePoint Online Embedding Power BI reports and report pages in SharePoint Online is now easier. With our new UI, you now have the option to directly select the workspace you want to embed in SharePoint, rather than copying and pasting the full URL. Simply navigate to SharePoint online, add Power BI content, and the new menu will allow you to browse workspaces and reports you have access to. Alongside embedding reports and report pages, you can now also embed a visual. Simply select the "Embed a single visual" toggle to select the specific visual you want to embed. Figure: Embedding a Power BI visual with the new SharePoint Online Experience. More details about these enhancements can be found in the Power BI and SharePoint Documentation. Developers + API’s Faster PBIP development with instant reloads and VS Code integration A faster PBIP editing experience is here. If you're working with Power BI Projects (PBIP), you can now edit project files outside Power BI Desktop and reload those changes instantly, without restarting the application. Power BI Desktop automatically detects changes made to your files and prompts you to apply them with a single click. Figure: Apply external changes prompt in Power BI Desktop. To make editing even easier, we've also added a built-in entry point that opens your project directly in Visual Studio Code. Figure: Open Visual Studio Code entry point in Power BI Desktop. Whether you're making edits in VS Code or using external tools to update your project files, you can now move seamlessly between your tools and Power BI Desktop without breaking your flow. Learn more Third party custom visuals Need more visual options for your report? Power BI reports support custom visuals built to fit your exact needs. Browse hundreds of options in AppSource, also available directly from Power BI Desktop, or develop your own and add it to a single report, share it across your organization, or publish it to AppSource for the community. You can even develop a custom visual tailored to your specific requirements with AI coding tools like GitHub Copilot Free. The following are some updates from the community-based custom visuals you can use in your reports today. PowerGantt Chart by Nova Silva The PowerGantt Chart helps teams plan, track, and communicate project timelines directly in Power BI. This month's update is shaped by your feedback, and we're grateful to the community of users whose suggestions keep guiding how the visual evolves. We've added three enhancements that give you finer control over how schedules and baselines are displayed. What's New? Baseline customization – Set the baseline bar size relative to the actual task bar and move milestone icons onto the baseline so planned versus actual delivery is easy to compare. Squared bar edges – Choose squared edges on item bars for a cleaner, more technical look. Vertical grouping – Group tasks vertically to organize large project structures by phase, team, or workstream. Whether you're managing product launches, construction schedules, or multi-team programs, these refinements make complex timelines easier to read briefly. Project managers, PMOs, and analysts gain sharper baseline comparisons, cleaner layouts, and better navigation across busy projects. Try the PowerGantt Chart for FREE now on your own project data by downloading it from the AppSource. Questions or remarks? Visit us at: NovaSilva. Slicer Panel by Powerviz Powerviz Slicer Panel is an advanced, all-in-one slicer that combines lists, dropdowns, sliders, buttons, date pickers, switches, and text slicers in one panel. With built-in templates, full customization, and import/export, it makes filtering smarter, simpler, and more interactive. Key Features Multiple Slicers in One Build: Bring together a wide variety of slicer types, including dropdown, expanded list, date, button, text, slider, and switch slicers, all within a single, unified panel, to filter your entire report from one place. Display Mode: Switch between Canvas, Pop-up, and Pop-up by Slicer. Orientation & Position: Arrange slicers and dock the panel top, bottom, left, or right. Panel Header: Show slicer icon, text, applied count, search, clear, and saved slicers. Slicer Action Icons: Edit, expand/collapse, sort, clear, rank, duplicate, or delete each slicer. Add Tabs: Group slicers into tabs for organized filtering. Saved Slicer: Store filter selections as reusable states. Conditional Formatting: Style slicer values with rule-based formatting. Advanced Search: Search across slicers and fields instantly. Other features included Templates, Import/ Export Themes, Search within Slicer, Ranking, Sorting, and more. Try Slicer Panel visual for FREE from App Source Check out all features of the visual Step-by-step instructions YouTube Video Learn more about visuals Follow Powerviz on LinkedIn Figure: From multiple slicer types to display modes, orientation, and saved slicers, every feature is designed to make your filtering more flexible, interactive, and effortless. Drill Down Map PRO by ZoomCharts When visualizing data with geographic coordinates, what better way to do it than literally placing it on a map? That’s why map charts are a growingly popular visualization type in Power BI reports, and the Drill Down Map PRO custom visual by ZoomCharts expands on the capabilities of map charts. Node Clustering: Multiple nearby nodes can create clusters and even display the values as pie charts. Simply zoom in to drill down. Base Layer Customization: Choose between AzureMaps or any custom tileserver, use your own images as the base layer, or disable it entirely. Custom Shape Layers: Enable up to 10 individually customizable shape layers. Use preset shapes or import your own KML/GeoJSON files. Conditional Formatting: Automatically apply color fill to each area by comparing their values against other shapes or by using each shape’s own reference value. And More: Paginated tooltips, custom tooltip fields, auras, node images, lasso tool. Figure: Reveal deeper insights with clustering, pie charts, custom shape layers, and flexible base map options tailored to your reporting needs. Figure: Customize every aspect of your map with conditional formatting, node images, auras, advanced tooltips, and intuitive selection tools. Drill Down Map PRO works incredibly well with other visuals by dynamically cross-filtering data, enabling you to build even more insightful and user-friendly Power BI reports. Get Drill Down Map PRO on AppSource Closing That's a wrap for the August 2026 Power BI Feature Summary. This month's release includes improvements across reporting, modeling, data connectivity, mobile experiences, embedded analytics, and developer workflows, along with continued investments in Copilot, accessibility, and Direct Lake capabilities. We hope these updates help you build, manage, and share data experiences more effectively. As always, we appreciate your feedback and look forward to hearing how you're using these features in your organization.
Diagnose Fabric Data Warehouse workloads with the SQL DW operations skill (Generally Available)
When a warehouse slows down, the investigation usually starts with more questions than answers. Was there a capacity spike? Did a specific query suddenly become expensive? Are requests failing, being canceled, or simply taking longer than usual? Answering those questions often requires switching between the Fabric Capacity Metrics app, Query Insights, and SQL pool diagnostics while manually correlating time ranges across multiple tools. The SQL DW operations skill brings those investigations into a single workflow and is now generally available. Available through the open-source Microsoft Fabric skills repository, the SQL DW operations skill lets you describe a problem in natural language using a compatible AI coding tool such as GitHub Copilot CLI. The skill runs bounded, read-only diagnostics and returns a structured diagnosis, supporting evidence, recommended actions, and validation steps. What you can do with the SQL DW operations skill The SQL DW operations skill helps you: failure-analysis: Separate failed queries from canceled requests, identify affected workloads, and resolve engine error codes. resource-consumers: Find recurring resource-consuming query patterns, regressions, and changes in execution volume or per-run cost. capacity-metrics-correlation: Connect a Capacity Metrics spike to warehouse activity in the same time window. pool-pressure: Diagnose contention and identify workloads that might benefit from custom SQL pools. lakehouse-health: Find lakehouse tables with small-file, deleted-row, or checkpoint issues. query-reference: Use the appropriate read-only system views and Query Insights queries for bounded operational analysis. scenarios: Combine the diagnostics into guided workflows for common warehouse incidents. Each response separates the diagnosis, evidence, ruled-out causes, recommendations, and customer follow-ups. Measurements are tied to their source, and zero-row results are treated as valid evidence instead of prompting an invented explanation. Common use cases Start with the operational question rather than selecting system views or writing diagnostic SQL. Investigate failed and canceled queries Analyze failed and canceled queries in SalesWarehouse during the last 24 hours. The skill uses Query Insights to identify affected users, applications, query patterns, and SQL pools. It resolves failed engine codes through sys.messages and keeps cancellations separate because they can reflect a user-initiated cancellation, a client timeout, or resource pressure. Explain a performance slowdown Explain why FinanceWarehouse was slow between 09:00 and 11:00 UTC yesterday. The skill checks SQL pool pressure, overlapping requests, CPU, elapsed time, and storage scans. It distinguishes contention from a directly expensive query or a broad increase in workload. Find resource-consuming query patterns Find the top resource-consuming queries in SalesWarehouse and compare them with the previous seven days. The skill groups requests by query shape and separates higher execution volume from increased per-run cost, new query patterns, and one-time expensive runs. Investigate a capacity spike Use the Fabric Capacity Metrics app to investigate the CU spike from 14:00 to 15:00 UTC, then identify expensive SQL users and query patterns. Following the warehouse metering update introduced in August 2026, Capacity Metrics shows when consumption occurred and how much was reported based on allocated warehouse compute over time. Query Insights explains what ran during the same period. The skill discovers the installed Capacity Metrics model, identifies a costly warehouse or SQL analytics endpoint, and analyzes Query Insights requests that overlap its time window. It doesn't join Capacity Metrics operation identifiers to Query Insights statement identifiers. Capacity consumption and warehouse CPU are complementary signals, not interchangeable measurements. Assess custom SQL pool candidates Assess whether recurring workloads in SalesWarehouse are candidates for custom SQL pools based on the last 30 days. If repeated pressure is associated with a consistent application name, such as an ingestion service or reporting application, the skill can recommend testing that workload in a custom SQL pool. It identifies the application to isolate and the pressure, latency, CPU, scan, and failure measures to compare before and after the pilot. Get started Prerequisites Before you start, make sure you have: GitHub Copilot CLI or another compatible AI coding tool. An active Fabric warehouse or lakehouse SQL analytics endpoint. Contributor or higher access to the workspace. The Microsoft Fabric Capacity Metrics app installed for capacity-spike investigations. Add the Microsoft Fabric skills marketplace in GitHub Copilot CLI: /plugin marketplace add microsoft/skills-for-fabric Install the Fabric skills bundle: /plugin install fabric-skills@fabric-collection Then open Copilot CLI in a project folder and describe the warehouse issue you want to investigate. Include the workspace, warehouse or SQL analytics endpoint, and UTC time range when possible. For detailed permissions, setup, supported scenarios, and diagnostic time limits, see Diagnose warehouse workloads with the SQL DW operations skill. Next Steps Install or update the Microsoft Fabric skills bundle. Review Query Insights in Fabric Data Warehouse. Review monitoring options for Fabric Data Warehouse. Learn about custom SQL pools.Fabric July 2026 Feature Summary
Welcome to the July 2026 Fabric update! This month brings new capabilities across the Fabric experience, from improved deployment and governance experiences to expanded Spark, Eventstream, and Real-Time Intelligence functionality. Whether you're building data pipelines, managing analytics workloads, or monitoring real-time operations, these updates help you work more efficiently and get more value from your data. _______________________________ Events and Announcements Get Fabric certified for FREE This is your chance to take the DP-600 (Fabric Analytics Engineer) or DP-700 (Fabric Data Engineer) certification exams for free. As part of Data Days, we have over 100 live sessions, more than 5 contests and challenges, and dozens of study groups and learning opportunities. And free Fabric exam vouchers. Available now through August 10, 2026. Request your voucher. Join us for FABCON and SQLCON in Barcelona, September 28 – October 1, 2026 Explore what’s possible with Microsoft Fabric and get up to speed on the latest in SQL, analytics, and AI. From 130 sessions and 4 keynotes to workshops, the expo, community spaces, and the Power BI DataViz World Championships, this is where the data community comes together. Learn directly from Microsoft and community experts shaping the future of Fabric and SQL. Register now and save €200 with code FABCMTY200. Fabric Platform Fabric-CICD tool v1.2.0 – new bulk publish mode (Preview) The June release of the fabric-cicd Python library, v1.2.0 introduces bulk publish mode. It lets fabric-cicd publish multiple items in a single API call using the Fabric bulk import API instead of publishing each item individually through a separate API call. This can make deployments more efficient. Why this matters Because dependencies are managed by the API during publication, bpublishinglish reduces the rigidity of item-type-based staging and better supports cross-item dependencies when logical ID references are used. It can reduce the number of item-specific parameter values you need to configure. For unsupported scenarios, fabric-cicd automatically falls back to the standard publishing flow, so you can try bulk publishing without manually switching deployment paths. To learn more, refer to fabric-cicd bulk option documentation and the v1.2.0 release notes. Change Git branch with at least the contributor role (Preview) Fabric Git integration now let’s any workspace member with at least the Contributor role switch the workspace's connected Git branch. Previously, this action required the workspace Admin role, which forced developers to either be over-permissioned or wait on an admin every time they moved between branches. Why this matters Developers no longer need Admin rights to switch branches. Removes a key blocker in the branch-out to existing workspace flow. Keeps developers as Contributors, aligned with least privilege governance. Fewer hand-offs to admins mean faster time to code. You can find this new capability under the Git integration settings. To learn more about Fabric Git integration new setting, refer to the Allow Contributors and Members to switch branches documentation. We refreshed the Fabric CI/CD documentation this month to make it easier to get started and to follow best practices: New CI/CD intro page — a reworked Introduction to CI/CD in Microsoft Fabric that walks through the platform layer by layer with a new enterprise reference architecture. New best practices guide — Understand the best practices for Fabric CI/CD brings together practical guidance for structuring workspaces, branching, and promoting content safely across environments. Auto-bind for Git integration — new guidance on cross-workspace dependency binding, covering how item dependencies automatically rebind when you branch out or update from Git, and which dependency types are supported. Actionable recommended actions for data owners in the OneLake catalog The Govern tab in the OneLake catalog gives data owners a health view of their data estate, along with recommended actions to improve it, such as increasing sensitivity label coverage, removing items that are no longer in use, handle failed refresh, and more. Until now, these recommendations have told data owners what to improve, but not which items were affected, leaving them to track the relevant entities down manually before they could act. That gap turned a clear recommendation into a manual investigation, and the action often stalled before it started. This release closes that gap. When you open a recommended action, you now see the specific items behind the recommendation, with everything you need to act on them in one place, and a direct path to open item details and resolve the issue at the source. Selecting a recommended action now opens a dedicated view that includes: A breakdown of the affected items — a visual summary of how many of your items are impacted. Why it matters — a short explanation of the governance impact. How to fix it — clear, step-by-step instructions for resolving the recommendation. Newly added A complete list of affected items — displayed in a table with key details, including Name, Last Refreshed, Owner, Location, Endorsement, and Sensitivity, making it easy to review and prioritize actions. Filter and search — narrow the list by keyword or filters to focus on the items you want to handle first. Open item details — jump straight to any affected item to make the change, instead of searching for it across your workspaces. The same enriched experience applies across the recommended actions in OneLake catalog Govern, helping you act on each one without leaving the catalog: Increase Sensitivity label coverage — find and label items that create potential security risks while unlabeled. Remove unused items — review items that weren't accessed or refreshed recently to keep your estate organized and reduce costs. Investigate items that failed to refresh — see which items failed to refresh so your data stays current and reliable. Add descriptions to your endorsed items — surface endorsed items without a description so people can better understand and use them. Apply relevant tags to your items — make items more discoverable by tagging the ones that are missing tags. Why it matters Governance only improves when recommendations turn into action. By bringing the affected items and a direct way to reach them into every recommended action, OneLake catalog Govern removes the guesswork from following through. Data owners can move from understanding a recommendation to resolving it in just a few steps, keeping their data estate secure, organized, discoverable, and trustworthy with far less effort. Learn more about the OneLake catalog Govern tab recommended actions. Data Engineering Microsoft Fabric Runtime 2.0 (Preview) Based on feedback received directly from our customers and partners, we have upgraded Fabric Runtime 2.0 to the latest and compatible stack: Apache Spark 4.1 Delta Lake 4.2 Python 3.13 These upgrades bring access to the newest enhancements, performance improvements, and ecosystem innovations, while also providing a longer support window for enterprise customers to build large-scale analytics and AI workloads on Fabric. With Runtime 2.0, customers can take advantage of: Latest Spark and Delta Lake capabilities. Improved compatibility across the modern data ecosystem. Better developer productivity and runtime performance. Future-ready platform investments aligned with long-term supportability. Learn more about Fabric Runtime 2.0. Fabric Runtime Release Channels (Preview) Fabric Runtime Release Channels provide a structured and transparent way for customers to test upcoming runtime changes before they become the default. This feature helps organizations validate their production workloads early with these new changes in early access, avoid unexpected disruptions, and gain better control over Spark runtime upgrades. Instead of receiving silent updates that might break your production workloads, you can opt in to an early access release channel, test your workloads in a development or staging environment, and confirm compatibility before the update becomes default. How release channels work Each Spark runtime has at least two public release channels: Default channel — This production-grade channel runs the default version of the runtime. All users automatically use this channel unless they opt in to early access. Early access channel — This production-grade channel includes upcoming updates and library changes that are scheduled to become the next default channel. You can opt in to test your workloads against upcoming changes. Once the designated validation window ends, the early access release channel automatically gets promoted to become the new default, and a fresh early access channel is introduced with another set of new changes — continuing the cycle. This model gives you a predictable testing window before changes become default for everyone. Why release channels matter Spark runtime updates can include library upgrades, security patches, dependency changes, or even operating system upgrades. While all updates pass internal quality checks before release, those checks can't capture all customer-specific variations and use cases. Early access channels let you identify potential issues early and work with Microsoft by creating a support ticket to address them before updates affect your production environment. Benefit Description ✔ Predictable updates Customers know exactly when a new runtime becomes available and have time to validate against it. ✔ Reduced risk Testing workloads on early access ensures compatibility before changes reach production. ✔ Better visibility Customers can easily tell which runtime version they're running, reference release notes, and verify upgrade timing. ✔ Improved quality and security You receive well-tested builds with security patches applied faster, giving you confidence in runtime stability. Learn more about Fabric Runtime Release Channels: Fabric Runtime Release Channels. Spark Diagnostic Emitter: Spark 4.1 Runtime Support and New Log Ingestion API The Fabric Apache Spark Diagnostic Emitter is now supported on the Fabric Runtime with Apache Spark 4.1. Customers can collect driver logs, executor logs, Spark event logs, and metrics from workloads running on the latest runtime and route them to Azure Event Hubs, Azure Blob Storage, or Azure Log Analytics — using the same spark.synapse.diagnostic.emitter.* configuration model, so existing emitter setups carry forward as customers upgrade. The emitter also now supports the Azure Monitor Log Ingestion API for sending diagnostics to Log Analytics, available on both Spark 3.5 and Spark 4.1 runtimes. The new AzureLogIngestion emitter type replaces the legacy HTTP Data Collector API path, providing a structured ingestion model with DCR/DCE-based authentication, schema definition, and routing into custom Log Analytics tables. Customers currently on the legacy AzureLogAnalytics type are encouraged to migrate — migration involves creating Data Collection Rule and Data Collection Endpoint resources and updating the Spark properties in the Fabric Environment. To learn more, refer to the Collect logs and metrics with Azure Log Analytics for migration guidance and Spark Diagnostic Emitter documentation. The Fabric Spark Operations Skill — AI-Assisted Spark Diagnostics, Open Source (Preview) The Fabric Spark Operations Skill (spark-operations-cli) is now available in the open-source Skills for Fabric library on GitHub (microsoft/skills-for-fabric). The skill brings AI-assisted, read-only diagnostics to Spark workloads in Fabric — troubleshoot failed notebooks and Spark jobs, stuck Livy sessions, and performance bottlenecks (OOM, shuffle, skew) in plain English from GitHub Copilot CLI, Claude Code, VS Code, Cursor, or other compatible AI tools. It returns severity-ranked findings with root cause analysis and fix recommendations, and includes an automated diagnostic workflow spanning job triage, log mining, Spark Advisor findings, and mitigations. Setup takes minutes: install the skill, run az login, and ask, "Why did my notebook fail last night?" Get started with Skills for Fabric in GitHub. Faster Python UDFs, Scala UDFs, and complex data types in the native execution engine (Generally Available) The native execution engine in Microsoft Fabric, which is generally available, now accelerates Python user-defined functions (UDFs), Scala UDFs, and complex data types such as arrays, maps, and structs. You get faster Spark processing for expressive code without changing your existing notebooks or jobs. Python UDFs have historically carried serialization overhead as data moves between the JVM and Python worker processes. The native execution engine optimizes that data path and keeps vectorized processing intact, so scalar and Pandas (vectorized) UDFs run faster automatically. Scala UDFs and queries that work with nested, complex data types benefit from the same native acceleration. Why this matters Faster UDF execution with no code changes. Vectorized Pandas UDFs see the largest gains. Complex types (arrays, maps, structs) run natively. Existing notebooks and jobs benefit automatically. To use Efficient Scaledown, enable the native execution engine on your Fabric Spark pool or environment. Existing Python and Scala UDFs and queries over complex types are accelerated without any code changes. To learn more, refer to the Python UDFs, Scala UDFs, and complex data types in the native execution engine documentation. Efficient Scaledown with the remote shuffle manager (Preview) Efficient Scaledown decouples Spark shuffle data from executor lifetime in Microsoft Fabric. Instead of pinning shuffle output to local executor disks, Fabric Spark routes large shuffles to Azure Blob Storage and migrates blocks off executors before they're released. Clusters scale down faster, compute costs drop, and jobs become more resilient — with no changes to your queries, notebooks, or pipelines. The feature combines four cooperating capabilities: the Remote Shuffle Manager writes and reads shuffle data to Azure Blob Storage; Shuffle Migration moves blocks off an executor before decommissioning instead of dropping them; a Decision Layer routes small shuffles to local disk and large shuffles to remote storage per stage; and AQE Shuffle Write lets Adaptive Query Execution shape partitioning the first time. Why this matters Clusters scale down faster after demand drops. Lower compute cost from quicker executor release. More resilient jobs with fewer stage retries. No changes to queries, notebooks, or pipelines. To use it, enable the native execution engine and run on Runtime 1.3 (Apache Spark 3.5) or later; autoscale is recommended. Remote Shuffle Manager spark.conf.set("spark.remote.shuffle.enabled", "true") Decision Layer — per-stage routing of local vs. remote shuffle spark.conf.set("spark.sql.rsm.decisionlayer.enabled.level", "stage") AQE participates in shuffle write spark.conf.set("spark.sql.adaptive.shuffleWrite.enabled", "true") Shuffle Migration on executor decommission spark.conf.set("spark.storage.decommission.shuffleBlocks.enabled", "true") spark.conf.set("spark.storage.decommission.shuffleBlocks.cleanup", "true") spark.conf.set("spark.storage.decommission.shuffleBlocks.migrateToFallbackStorage", "true") spark.conf.set("spark.storage.decommission.fallbackStorage.cleanUp", "true") To learn more, refer to the Efficient Scaledown and remote shuffle manager in Microsoft Fabric documentation. Customer-managed key encryption for Spark shuffle data on disk (Generally Available) Microsoft Fabric Spark now generally supports customer-managed keys for Spark jobs through a disk encryption set. Shuffle data written to cluster disks during a Spark job is encrypted with a key you supply and control, giving you ownership of the encryption material that protects intermediate data at rest. With a disk encryption set configured, the cluster disks that hold Spark shuffle data are encrypted using your customer-managed key rather than a platform-managed key. You manage the key lifecycle — including rotation and access — in your own key vault, so encryption of intermediate Spark data follows your organization's key management policies. Why this matters You control the key protecting shuffle data. Intermediate Spark data on disk is encrypted at rest. Key lifecycle and rotation stay in your control. Encryption aligns with your key management policies. To use it, configure a disk encryption set backed by your customer-managed key and associate it with your Fabric Spark configuration. Spark jobs then encrypt shuffle data on cluster disks with your key. To learn more, refer to the Customer-managed key encryption for Fabric Spark documentation. Query your data instantly with the Lakehouse Query Explorer (Generally Available) The Lakehouse Query Explorer is a new, fully integrated query editor built directly into the Lakehouse experience in Microsoft Fabric. You can now write and run Spark SQL queries right where your data lives — no need to switch to a SQL endpoint or spin up a notebook for quick exploration. Whether you’re validating datasets, iterating logic, or exploring patterns, Query Explorer keeps you in flow. orer with the new integrated Query Explorer. Key capabilities Fast, lightweight Spark execution powered by the Lakehouse Livy endpoint. IntelliSense + rich editing experience for faster query authoring. Query across schemas and lakehouses in a single tab. Built-in results grid + inline charts to explore results instantly. Multiple dynamic tabs to analyze different slices of data side-by-side. From quick lookups to multi-table exploration, Query Explorer makes working with Lakehouse data faster and more intuitive—right from the explorer. Learn more and get started with the Lakehouse Query Explorer documentation. Analytics and Insights for Materialized lake views (Generally Available) Analytics and Insights bring continuous refresh intelligence to your lakehouse — two new tabs beside Recent run(s) that shift you from reacting to individual failures to staying ahead of performance drift, rising costs, and silent inefficiencies across your entire materialized lake view estate. The Recent run(s) page tells you what happened in a single execution, including which views succeeded, which failed, and how long the job took. That's valuable when something breaks, but it doesn't answer the questions that matter most for day-to-day operations. Are my durations stable or slowly climbing? Is a new error class appearing and spreading across views? Are my schedules still aligned with how often upstream data actually lands? Am I paying for full refreshes on views that could run incrementally? These are the questions that separate a well-tuned deployment from one that quietly accumulates cost and risk until something finally breaks loudly enough to notice. The Recent run(s) page answers all of them. The Analytics tab transforms your run history into trend lines, distributions, and comparisons you can read at a glance: duration trajectories, success-rate shifts, error-class frequency over time. The Insights tab goes further by watching those same patterns the way a seasoned reliability engineer would, recognizing the signatures behind slow, failing, or wasteful runs, and providing a prioritized list of worthwhile changes. Each recommendation names the affected view, explains why it's flagged, and estimates the payoff in runtime savings, so you can act in seconds rather than investigate for hours. Together, they move refresh management from a reactive, break-fix posture to a proactive, continuously improving one — keeping your materialized lake views fast, healthy, and cost-efficient as your estate grows. To learn more, refer to analytics charts, insight categories in Materialized Lake Views. Introducing Event-Driven Refresh for Materialized Lake Views (Preview) Event-driven refresh brings responsive refresh intelligence to your lakehouse — a new scheduling mode alongside time-based schedules that shifts you from refreshing on the clock to refreshing the moment your data is ready, so your materialized lake views reflect reality instead of an arbitrary calendar. Time-based schedules tell your views when to run: every hour, every morning, every night. That's dependable when upstream data lands like clockwork, but it doesn't answer the questions that matter most for day-to-day operations. Did the ingestion pipeline that feeds this view finish before I refreshed it? Am I recomputing gold-layer views on a fixed cadence while the source data only changes twice a day? Am I paying for refreshes that run before new data has even arrived — or worse, serving stale results because the next scheduled slot is still hours away? These are the questions that separate a refresh strategy tuned to your data from one that quietly burns compute on empty runs and lags the moments that matter. Event-driven refresh answers all of them. You bind a view or a lineage sub-chain to the events that should drive it, and we support two types of events: OneLake events — all file and folder events are supported, so a refresh can fire the moment data lands in OneLake (file or folder creation, update, and more). Job events — Pipeline and Notebook events are supported, so a refresh can fire when the ingestion job that feeds your views completes. The moment an event fires, Fabric resolves the dependency chain and refreshes exactly the views that depend on it. Each trigger names the source event, scopes precisely to the affected lineage, and can react to success or failure, so a stalled upstream job never silently cascades into stale downstream reports. Together with multi-schedule support, event-driven refresh moves refresh management from a fixed-clock, guess-the-cadence posture to a responsive, data-driven one — keeping your materialized lake views fresh the instant new data arrives, and idle when it hasn't, as your estate grows. Learn more and get started with the Schedule a Materialized Lake View Refresh documentation. Data Science AI functions: new models, no package dependency, better usage stats (Generally Available) Fabric AI Functions now use gpt-5-mini as the default model, with “low” reasoning enabled. This powers AI Functions across pandas, PySpark, Data Warehouse, and Dataflows Gen2. For more sophisticated transformations, users may configure gpt-5.1 or tune the reasoning_effort parameter for additional compute and higher-quality results. The gpt-4.1 model has been retired. Pipelines pinned to gpt-4.1 have migrated to gpt-5.1, and those pinned to gpt-4.1-mini migrated to gpt-5-mini. We’ve also simplified PySpark AI Function chaining. The PySpark .ai interface now stays bound to the result schema, so chains like summarize → classify no longer require intermediate DataFrames. In addition, PySpark now supports df.ai.stats for detailed token usage after any AI function call, including reasoning token breakdowns. For pandas, AI Functions no longer require the openai-python package. Capacity-limited rows are surfaced as CapacityExceededResult, enabling clean retries via aifunc.split_results. To learn more, refer to the AI Functions documentation. Data Warehouse Lakehouse table health check (Generally Available) Lakehouse Table Health Check gives you a simple, T-SQL–based way to validate whether your Lakehouse tables are optimized for the SQL analytics endpoint. A single stored procedure surfaces common layout issues, such as small files and fragmentation, offering the insights you need to determine if your tables need to be optimized. You can integrate health checks into pipelines and operational workflows, enabling proactive, at-scale optimization instead of reactive troubleshooting. -- Run a health check on a Lakehouse table from the SQL analytics endpoint EXEC sp_get_table_health_metrics 'dbo.FactSales'; aluate table health. To learn more about sp_get_table_health_metrics and how to integrate it into your Pipelines to optimize your table only if anomalies are detected, refer to sys.sp_get_table_health_metrics (Transact-SQL). Scalar User‑Defined Functions - Procedural computation for analytical SQL (Preview) Scalar User-Defined Functions now support procedural computation — including loops, multiple return paths, and rich IF/THEN/ELSE branching — running natively within the warehouse engine. Computation-based Scalar UDFs are designed for analytical query shapes, integrating naturally with CTEs, GROUP BY, HAVING, and ORDER BY, and executing efficiently at data warehouse scale. Defining business rules once, in SQL, and reuse them across queries, reports, and pipelines. To learn more, refer to the Create Function documentation. Usage-based resource estimations (Generally Available) The Query Optimizer uses learned resource estimation to correct underestimation in T-SQL query plans. It saves actual cardinalities from past executions and automatically adjusts row count estimates in subsequent runs. With this release, the optimizer will also begin correcting overestimations — closing the loop in accurate cardinalities, leading to more efficient resource requests and improved concurrency. Real-Time Intelligence Improved tile error experience in Real-Time Dashboard (Generally Available) We improved the way tile errors appear in Real-Time Dashboards to make them clearer, calmer, and easier to act on. Instead of showing a disruptive red error state, tiles now use a neutral grey error state with a short category header, such as Syntax error, Semantic error, Data source issue, Network error, or Something went wrong. Users can select Details directly from the tile to open a popover with the full engine error message, shown exactly as received. This keeps the dashboard readable while still making the technical details easy to access, copy, and share when needed. This update helps users understand what went wrong faster, while ensuring that one failed tile does not block or visually overwhelm the rest of the dashboard. To learn more, refer to the Troubleshoot Real-Time Dashboard Tile Errors documentation. Folders in Eventhouse tree (Generally Available) Folder support in the Eventhouse tree is now available through the UI. Previously, folders could only be created and managed via code. With this update, you can now organize your Eventhouse directly from the tree, making it easier to manage assets at scale. You can group tables, shortcuts, materialized views, functions, and data streams into a structured hierarchy, improving navigation and reducing clutter. Key capabilities Create, rename, and delete folders from the UI. Organize assets into folders. Move items via the context menu (⋯ → Move to). Create folders inline while moving items. This allows for a simplified and more intuitive way of managing growing Eventhouses. To learn more, refer to the Manage and monitor a KQL database table documentation. Eventstream connector private network support (Generally Available) Data is a critical asset for organizations, and access to real-time data is increasingly essential. However, many high-value data sources reside in private network environments — cloud virtual networks or on-premises infrastructure — particularly in highly regulated industries such as banking, finance, and telecommunications, where strict security and compliance requirements are mandatory. Eventstream's private network support establishes a secure, managed bridge using your Azure virtual network and VNet injection, allowing Eventstream streaming connectors to run inside your virtual network and reach private sources without opening them to the public internet. Whether your data sources reside in on-premises networks, private networks on third-party cloud services, or private networks on Azure, you can connect the bridge Azure virtual network to your source's private network using suitable connectivity options — such as VPN or ExpressRoute for on-premises environments, and private endpoints or network peering for Azure-based sources — enabling Eventstream connectors to securely ingest real-time data from these protected environments into Fabric. The solution leverages a new concept — Streaming virtual network data gateways — which abstracts the bridge Azure virtual network and subnet resource within Fabric. By creating a connector with a streaming virtual network data gateway associating it with your connection, the Eventstream connector is provisioned within your virtual network, ensuring secure communication with your private data sources. Once real-time data from your private network source is securely brought into Fabric Eventstream, you can fully leverage the comprehensive analytics tools in Fabric Real-Time Intelligence to power your real-time scenarios with enterprise-grade security. To learn more about configuration and advanced scenarios, refer to the Eventstream private network streaming guide. Azure Event Hubs source in Eventstream now supports workspace identity authentication (Preview) Currently, when configuring an Azure Event Hubs source in Eventstream, the only supported authentication method is Shared Access Key — a connection string containing static credentials. While simple to set up, shared access keys present several security risks in production environments: they have unlimited lifetime unless manually rotated, provide no per-user or per-application identity, and if accidentally leaked through source code, configuration files, or third-party sharing, grant full access to anyone who obtains them. Revoking a compromised key requires regeneration, which disrupts all services depending on it — with no straightforward way to audit which clients used the key. To address these challenges, we're introducing Workspace Identity as a new authentication option for the Azure Event Hubs source connector (Extended features) in Eventstream (Preview). A Fabric workspace identity is an automatically managed service principal associated with your workspace. Fabric manages the credentials entirely — there are no secrets to store, rotate, or risk leaking. It integrates with Microsoft Entra ID, providing identity-based access with full audit trails, fine-grained role-based access control, and automatic credential lifecycle management. To use workspace identity authentication with your Azure Event Hubs source in Eventstream: Navigate to your workspace settings and create a workspace identity on the Workspace identity tab. In your Azure Event Hub namespace, assign the appropriate role (e.g., Azure Event Hubs Data Receiver) to the workspace identity's service principal. When adding an Azure Event Hubs source in Eventstream, select Workspace Identity as the authentication method — no connection string or key is needed. Eventstream will automatically obtain tokens using the workspace identity to securely connect to your Event Hub, eliminating credential management overhead while strengthening your security posture. To learn more about the configuration, refer to the Azure Event Hubs source extended connector configuration documentation. Custom CA and mTLS support in Eventstream streaming connectors (Generally Available) Fabric Eventstream under Real-Time Intelligence provides various streaming connectors, enabling the integration of real-time data from popular sources into Fabric. When the Eventstream connector client establishes a connection with sources, it is required to implement TLS or mTLS encryption to fulfil the necessary security standards. Many organizations use certificates issued by private or internal Certificate Authorities or require mutual TLS (mTLS) authentication where both the client and server verify each other's identity before transmitting data. Without custom CA and mTLS support, Eventstream connectors cannot connect to these secured source systems. The Custom CA and mTLS support feature, is now generally available for MQTT, Apache Kafka, AWS MSK, and Confluent Cloud for Apache Kafka source connectors. Customers can specify their custom CA and client certificates managed in their own Azure Key Vault when configuring their source in Eventstream. Once specified, Eventstream connector will fetch the certificates from the customer's Azure Key Vault and use them to establish a mutually authenticated, encrypted connection — enabling secure, compliant real-time data ingestion across all supported streaming sources. To learn more about the configuration, refer to the Eventstream sources overview page and choose the corresponding source. Introducing the Oracle CDC connector for Eventstream (Preview) Eventstream now introduces the Oracle Database Change Data Capture (CDC) connector, enabling you to stream database change events directly from any Oracle Database — whether running in the cloud or on-premises — into Eventstream for real-time processing and analytics. Many organizations run important operational workloads on Oracle Database and need to react to changes as they happen. With the Oracle CDC connector, you can continuously capture change events from Oracle Database and bring them into Fabric without building custom polling applications or managing separate integration services. With the Oracle CDC connector, you can: Capture and stream databases changes from Oracle Database into Fabric in real time. Connect to Oracle databases running either on-premises or in the cloud. Process incoming change events using Eventstream transformations. Route processed change events to supported destinations such as Eventhouse, Lakehouse, Activator, or custom endpoints. This capability helps you build real-time analytics and event-driven applications from Oracle data. For example, you can route transaction changes to an Eventhouse Kusto table for operational analysis, send selected events to Activator for alerting, or combine Oracle change events with other streaming sources in the same eventstream. To learn more about Eventstream Oracle CDC connector, refer to Add Oracle Database CDC source to an eventstream (preview). Eventhouse update policies now support referencing accelerated shortcuts in update policies (Generally Available) Eventhouse update policies now support accelerated shortcuts in update policy queries, enabling ingestion-time enrichment scenarios. Use this for dimension lookups, such as enriching ingested fact events with customer, device, or product attributes stored in OneLake shortcut data. The shortcut-backed external table must have Query Acceleration Policy enabled, and Hot must cover all data. For update policy scenarios, set: .alter external table DimCustomer policy query_acceleration '{"IsEnabled":true,"Hot":"36500.00:00:00"}' Then join to it from the update policy query: .alter table EnrichedEvents policy update '[{ "IsEnabled":true, "Source":"RawEvents", "Query":"RawEvents | lookup kind=leftouter (external_table(''DimCustomer'')) on CustomerId","IsTransactional":true, "PropagateIngestionProperties":false }]' Processing uses the authorization context captured in the system-populated OwnerPrincipalDetails property: the user who creates or alters the update policy must have access to the shortcut data. This enables ingestion-time enrichment with governed shortcut data without separate orchestration. Shortcut Tables in Eventhouse Now Automatically Synchronize Schema Changes To help maintain consistency between source data and shortcut tables, the default schema synchronization behavior for Eventhouse shortcut tables is changing. Previously, schema changes made to the source table were not automatically propagated to the shortcut table unless schema synchronization was explicitly enabled. As a result, source and shortcut schemas could diverge over time. With this update, all new and existing shortcut tables in Eventhouse automatically synchronize schema changes from their source table by default, including: Adding new columns Changing column data types Renaming columns Deleting columns Automatic schema synchronization helps ensure that shortcut tables remain aligned with the source schema, preserving the latest business context and reducing manual maintenance. Customers who prefer to disable automatic schema synchronization can continue to control this behavior using the existing KQL management command: .create-or-alter external table ExternalTable kind=delta ( h@'https://storageaccount.blob.core.windows.net/container1;secretKey' ) with (AutoUpdateSchema=false) Note: Because schema changes are now automatically propagated, queries, dashboards, and downstream workloads may require updates if they reference columns that are renamed, removed, or otherwise modified in the source table. Investigator Insights in Operations Agent (Preview) When an anomaly is detected, understanding what caused it is often the hardest part. Investigator insights are designed to make that easier by analyzing the surrounding data and surfacing relevant context. When the operations agent detects that a rule has been met, there is an option to run an investigation in the background to identify correlated signals and patterns. Instead of manually digging through telemetry, you get a guided view into what changed, what stood out, and what may have contributed to the issue. This helps you move more quickly from detection to understanding. You can access these insights directly from Teams. Open the agent’s message and select Investigate further to generate a detailed analysis. The investigation provides a structured view of what happened: Investigation scope shows which tables were analyzed, whether from a single source or across related datasets. Key observations highlight the most important findings, including actual values, deviations from baseline, and notable trends or outliers. Pattern analysis surfaces meaningful changes around the time of the anomaly, such as dimensions with significant shifts, and clearly call out when no strong patterns are identified. Together, these insights help you quickly understand not just that something went wrong, but why it happened. To learn more, refer to the Operations Agent Actions documentation. Anomaly Detector Configurations Pane As you build on top of your data, it is often just as important to understand what already exists as it is to create something new. This update makes it easier to discover and build existing anomaly detection configurations. You can now view all anomaly detection configurations that have already been created for a given data source in one place. This lightweight experience gives you quick visibility into how anomaly detection is currently set up, helping you avoid duplicate work and better understand how others are using the data. From this view, you can explore existing configurations or create a new one if your use case is not yet covered. This makes it simple to extend existing setups or start fresh when needed, all without leaving the context of your data source. By making configurations easier to discover and reuse, this experience helps streamline workflows and ensures you can move quickly from exploration to action. To learn more, refer to the Anomaly Detection in Real-Time Intelligence documentation. Ingestion time stamp in Anomaly Detector (Preview) Working with anomaly detection often assumes your data already includes a clean, reliable timestamp. With this update, you can now use the system-generated ingestion time as the timestamp for anomaly detection. This means that even if your dataset does not include a dedicated timestamp column, you can still run analysis without needing to modify or preprocess your data. This is especially helpful for scenarios where events are ingested in real time or where timestamps are missing, inconsistent, or not trustworthy. Instead of blocking data preparation, you can rely on ingestion time to move forward with detection and start identifying meaningful patterns right away. By reducing setup requirements and removing a common dependency on source data quality, this capability makes it easier to apply anomaly detection across a wider range of use cases. To learn more, refer to the Anomaly Detection in Real-Time Intelligence documentation. Configuring Anomaly Detection without a Group by Column (Preview) With this update, Anomaly Detection now supports scenarios where you choose not to use a group by column. This provides an additional configuration option for datasets that already represent a single stream of data, allowing you to apply anomaly detection directly to the metric without first identifying a grouping dimension. This added flexibility helps the configuration experience better align with how your data is structured. Whether you are monitoring a single device, tracking a specific service, or analyzing a focused dataset, you can now create anomaly detectors without requiring a group-by column. At the same time, grouping remains available for scenarios where you want to monitor and compare multiple entities within the same dataset. To learn more, refer to the Anomaly Detection in Real-Time Intelligence documentation. Introducing Fabric Maps Tilesets: High-Performance Visualization for Large Geospatial Datasets (Generally Available) Have location-based data sitting in OneLake but no simple way to see it come alive on a map? With Fabric Maps Tilesets, you can now turn that data into fast, interactive map experiences directly inside Microsoft Fabric. The Tileset Builder lets you create map-ready PMTiles from OneLake data without custom code, manual exports, or separate geospatial infrastructure making it easier for teams to explore large geospatial datasets, uncover patterns, and bring location intelligence into the analytics workflows they already use. Organizations can rely on Microsoft Fabric to manage operational data, analytics workflows, and business reporting. When that data includes locations, routes, assets, boundaries, or events with a location context, teams need a simple way to visualize it on a map. Traditionally, this required separate geospatial pipelines, custom polling services, or manual exports. Leveraging a Fabric Map removes complexity by allowing organizations to create and refresh map-ready tilesets from data already stored in OneLake. Try creating your own Tileset using the following steps: Connect to a lakehouse and select source files Configure tileset metadata Configure layer settings Tileset schedule (Preview) Review and create tileset What is a Tileset? Tilesets are map-optimized representations of geospatial data. Instead of trying to load a large geospatial data file all at once, data is divided into small tiles that are loaded and rendered as needed while users zoom and pan across the map. This makes tilesets especially useful for large datasets, such as infrastructure networks, delivery routes, asset locations, service areas, or operational events. Fabric Maps Tileset Builder Capabilities Build map-ready tilesets directly from OneLake data. Visualize large geospatial datasets with high performance. Enable smooth, interactive map experiences at scale. Keep map content synchronized with source data. Eliminate manual exports and external geospatial processing workflows. Integrate native geospatial visualization into existing Fabric data workflows. Common scenarios Tilesets help teams turn large location-based datasets into fast, interactive map experiences. Utility and energy companies can visualize nationwide power line grid systems as a single dataset to monitor field assets and service coverage. Supply chain and logistics teams can explore daily routes, regional boundaries, and operational areas. Retailers can analyze store territories, expansion opportunities, and new developments. Because the data stays connected to Fabric, these map experiences become part of the broader analytics workflow — not a separate geospatial process. To get started, refer to How to create tilesets. Cross-domain intelligence with Azure Monitor data in Microsoft Fabric (Preview) Have you ever detected an issue in your systems but struggled to understand what it meant for the business? As systems grow more complex, this gap becomes harder to bridge. Incidents no longer affect just systems; they affect customers, revenue, and operations in real time. Azure Monitor Logs mirroring into Microsoft Fabric helps close that gap. In just a few steps, telemetry from Log Analytics workspaces becomes available in OneLake alongside business and operational data — without duplication and with near real-time availability. This creates the foundation for Cross-domain insights and actions: Bring observability, operational, and business data together in Eventhouse for real-time analysis. The same unified data can be used by Real-Time Dashboards for investigation and by operations agents to recommend and drive actions informed by both business and observability context. For example, an operations team can identify that a check-in kiosk outage is impacting high-value customers and act before customer impact grows. Advanced Fabric analytics Apply tools like Spark and Power BI for long-term analysis, machine learning, and a wide range of analytical scenarios. For example, an operations team can create a Power BI report showing trends in customer impact and cost, helping management make informed decisions on resource allocation and understand the true cost of application failures. Together, these capabilities help organizations move from isolated technical signals to business-aware insights, decisions, and actions. To learn more, refer to Cross-domain intelligence with Azure Monitor data in Microsoft Fabric (Preview). Until next month That's a wrap for the July 2026 Microsoft Fabric Monthly Update. As always, we'll continue sharing new capabilities, enhancements, and improvements across Microsoft Fabric in future monthly updates. Thank you for being part of the Fabric community!
