Power BI July 2026 Feature Summary
Welcome to the July 2026 Power BI update! This month, we're introducing new capabilities across reporting, modeling, mobile, and embedded analytics. Highlights include expanded modern visual defaults, conditional formatting improvements for line charts and legends, new org app capabilities and APIs, Model Options in the Power BI Service, and TMDL View on the web. Let's take a look at what's new this month. Download Power BI Desktop Events and Announcements Your Road to the Power BI DataViz World Championships Round Two of the Power BI DataViz World Championships is now open, with Round Three still ahead. Plus, one wildcard finalist will be selected from standout submissions across all stages of the championship. Is this your moment? The only way to find out is to enter. Every submission is a chance to learn something new, connect with the community, and showcase your creativity. You might surprise yourself along the way and earn a place in the finals. Join now. Data Days Goes Through August 7th Looking for your next Power BI breakthrough? Data Days brings together the best of data, AI, and analytics in free live sessions. Learn new skills, get inspired, and connect with the community. Learn more. Will we see you in Barcelona? 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 utilizing 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. Reporting Update your report defaults and use color palettes with the modern visual defaults (Preview) We've expanded the Modern Visual Defaults preview with a new Customize current theme experience directly in the Format pane. You can now adjust report-wide defaults that apply to all visuals on every page—without manually styling each one. Choose from color palettes to change your report's colors while preserving other customizations or use the expanded visual-level options to style or toggle elements like background, border, header icons, tooltips and shadow on or off across your entire report. You can also fine-tune border thickness, corner radius, and default padding. Every new visual you add automatically inherits your styling, so your report stays consistent as you build. Beyond visuals, you can update page size, background color, and wallpaper across all pages at once, and apply filter pane formatting to current and new pages. These customizations create a custom theme layered on top of your base theme. Styling applied to individual pages or visuals takes precedence, and you can reset any setting to re-enforce the theme defaults. When you're finished, export your custom theme to use in other reports or add it to your organizational themes so others can create uniformly styled reports. To get started, enable the Modern visual defaults and customize theme improvements preview in Options, then when nothing is selected on the page, choose the Customize current theme option in the Format pane. Visit our documentation to learn more about visual defaults in Power BI reports. Conditional formatting for line charts and legends (Generally Available) One of the highly requested features from the Power BI community is here: conditional formatting now extends to line charts and visuals with legends. This update applies to bar charts, column charts, pie charts, donut charts, and line charts—when these visuals have a legend, you can conditionally format their colors. For line charts, format line colors using a gradient based on the total value of each line, or by the category it represents. For example, when overlaying multiple years by month, apply a gradient that displays the most recent year in blue while older years fade to lighter shades of grey. Markers and series labels automatically inherit the conditional formatting until you choose to color them differently. For visuals with category legends, you can ensure consistent colors across your entire report. If multiple visuals use Segment as their legend, color each segment using a single DAX measure. Every visual displays the same color for the same category. When you need to change a segment's color, update the measure once and all visuals reflect the new color instantly. Add data-driven colors to your reports today! Learn more about how to use conditional formatting in other places at Conditional formatting in Power BI visuals. Report page tab navigation improvements (Generally Available) Managing reports with many pages is now easier. Previously, when you dragged a page tab to reorder it, you couldn't move it past the visible tabs, making it difficult to move a page from the end of your report to the beginning. Now, when you drag a page tab toward the edge of the tab bar, the tabs continue to scroll until you reach the position where you want to place it. The arrow buttons that page through report tabs are also more precise, ensuring you see each report page tab as you navigate. These improvements make it faster to organize and navigate reports with dozens of pages. Learn more about working with report pages in Report View in Power BI Desktop. Simplified title spacing for visuals We've streamlined how spacing works in the title area of your visuals. The spacing properties now use a consistent "space above" approach—Space above subtitle, Space above divider, and Space above plot area—instead of the previous "space below" settings. This makes it easier to understand and control the gaps between title elements, and ensures visuals look consistent even when they have different combinations of subtitle and divider. Your existing reports and themes automatically adjust to the new settings without any change to their appearance. If you've configured title spacing in a custom theme, we recommend updating it to use the new "space above" properties for clarity and consistency going forward. Learn more about customizing visualization titles. Visuals calculations LOOKUP function now supports INFERRED mode for associated columns The LOOKUP function in visual calculations now accepts an optional AssociatedColumnsBehavior parameter with two values: EXPLICIT (default) and INFERRED. When you use INFERRED, the engine automatically identifies which axis columns are functionally determined by the coordinates you supply and fills them in before evaluating the lookup. This means a lookup that specifies Year, Quarter, and Month can return a value across all rows in the matrix, rather than only on the row where the current context exactly matches. When the parameter is omitted, LOOKUP behaves exactly as it does today—existing expressions continue to work without changes. When you explicitly supply a value for a column that would have been inferred, LOOKUP validates your value against the inferred value and returns BLANK if they conflict, providing consistent error handling across both modes. Org apps with audiences (Generally Available) With support for audiences, app creators can deliver different content experiences to different groups of users within the same app, making it easier to personalize content while managing a single app experience. Learn more in these blog posts, Org apps with audiences for Power BI and Fabric (Generally Available) or learn more about how org apps and audiences work. Bookmarks support for reports in org apps Users can now access bookmarks when viewing interactive reports in org apps. This includes both report bookmarks created by report authors and personal bookmarks users create for themselves, making it easy to return to saved report views with the filters, selections, and visual state they care about most. To learn more about bookmarks in the Power BI service, refer to the documentation. Storytelling (PowerPoint embed) support for reports and visuals in org apps Reports in org apps now support Storytelling in PowerPoint, allowing users to add live report pages and visuals from org app content into PowerPoint presentations. This extends the existing Storytelling experience to reports distributed through org apps, making it easier to present and share curated content across your organization. Learn more about how you can enhance your data storytelling in PowerPoint with live, interactive Power BI data. CRUD APIs (REST) for org apps and audiences Developers and administrators can now programmatically manage org apps and audiences using Microsoft Fabric REST APIs. New APIs support create, read, update, delete, and list operations for both org app items and audience items, enabling automation, deployment, and management scenarios at scale. Learn more about the org app item definition and org app audience item definition. CRUD APIs (REST) for paginated reports Paginated reports can now be managed through Microsoft Fabric REST APIs. New support for create, read, update, delete, and list operations enables developers and administrators to automate report lifecycle management, deployment workflows, and administrative processes. Learn more about the paginated report APIs and the paginated report definition. Modeling Measure descriptions in line with triple-slash (///) comments Good descriptions make a semantic model self-documenting. Until now, adding them was a two-step process: you authored your measures in DAX query view, then switched over to Model Explorer just to type the description in. Yet with the latest Desktop release, you can now add measure descriptions right where you write your measures, using the same triple-slash (///) syntax that TMDL already supports for both measure and function descriptions. Add one or more /// comment lines directly above your MEASURE definition, select “Update model with changes”, and the description is written straight into the model along with the measure definition. DEFINE /// Year-over-year sales growth, as a percentage. /// Returns BLANK when there is no prior-year value. MEASURE Sales[YoY Growth %] = ... Once it’s saved, the description travels with the measure: hover over the measure in DAX query view, in Model Explorer, or in the Data pane, and the description shows up in the tooltip next to the measure name, and it carries through to the quick queries DAX query view generates. Specifically, when you right-click a measure and choose Quick queries, and then select Define and evaluate (or any other Define option), Power BI generates a DEFINE MEASURE statement in DAX Query View that includes the description as /// comments above the measure definition, as in the following screenshot. It’s a small addition with a nice payoff — descriptions become part of the authoring flow, consistent across measures and UDFs. And it’s non-breaking: if you already use /// inside a measure body, nothing changes there. This is specifically about the comment lines above the MEASURE definition. Try it out in the July release of Power BI Desktop. For more details about how to work with measures in DAX query view, refer to Work with DAX query view in Power BI documentation. Model Options now available in Power BI Service We’re continuing to bring more semantic modeling capabilities to the web! With this update, the Model options dialog will now be available directly in web modeling, giving you greater control over key modeling-level settings, without needing to switch back to Power BI Desktop. The new Options dialog enables you to configure important behaviors and metadata that are defined at the model level (current file) in a centralized experience. The new Model Options dialog in web modeling includes the following settings: Type detection: enable/disable automatic detection of column types and headers for unstructured sources. Relationships: configure auto-detection behavior for relationships in the model. Time intelligence: enable/disable automatic creation of hidden date tables. Parallel loading of tables: set the concurrency mode used when loading tables. Locale for import: set the regional settings used to interpret numbers, dates and time in imported text. DirectQuery connections to this semantic model: Toggle whether other semantic models can connect to this semantic model using DirectQuery. When enabled, this model cannot be used as a DirectQuery source. (Direct Query only) Maximum connections per data source: limit how many concurrent DirectQuery connections can be opened to a single data source. For more details, refer to the Model Options in Power BI Service documentation. New: The semantic model settings pane becomes the default (Preview) Starting in August, when you open the settings for a semantic model in the Power BI Service, they open in the new settings pane instead of the full settings page. This change updates the default experience: the settings pane opens beside your content, so you can update refresh schedules, credentials, and other options without leaving your workspace or losing your current work context. Everything you manage on the settings page today is available in the pane, so the settings are fully configurable. If you'd rather keep using the legacy settings page, you can switch back at any time from within the pane. The settings pane first arrived in preview in May. Refer to the original semantic model settings pane announcement to learn what it offers and to review how semantic model settings work, refer to Edit Semantic Models In The Power BI Service. TMDL View on the Web Now, developers can script, modify, and apply changes to model objects directly in a code editor using Tabular Model Definition Language (TMDL), without switching to Desktop or downloading model files. This experience provides immediate, code‑level visibility into all semantic model metadata (tables, measures, relationships, and more), enabling bulk edits, automation of repetitive tasks, and the reuse of definitions to improve consistency and productivity. For more details about TMDL view and the full list of capabilities in this release, refer to the documentation: Use Tabular Model Definition Language (TMDL) view in Power BI Desktop. Mobile Org app audiences are now in mobile apps Org app audiences in the Power BI mobile app extend a tailored org app experience from Power BI Service to users on iOS and Android. With this update, mobile and tablet users can open an org app and see and navigate content based on their assigned audience, so they can quickly find the reports, dashboards, and Power BI content most relevant to their role while on the go. Audiences in org apps help organizations deliver the right content to the right people from a single governed app experience. Instead of creating and managing multiple apps, app creators can define multiple audiences, control which items each audience sees, and tailor navigation for each group. On mobile, this results in a cleaner, more focused experience on smaller screens. Executives, managers, frontline workers, and department teams can each land in an app experience curated for their needs while still benefiting from centralized management of a single org app. This update helps close a key gap for customers adopting org apps. Org apps are the recommended way to package and distribute Power BI and Fabric content, and mobile access is essential for users who rely on Power BI away from their desks or in frontline scenarios. Organizations that use audiences to personalize org app content can now extend those tailored experiences to Power BI mobile apps. To learn more about org apps and audiences, refer to the org apps blog, Org apps with audiences for Power BI and Fabric. 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. These are some updates from the community-based custom visuals you can use in your reports today. Drill Down Scatter PRO by ZoomCharts Drill Down Scatter PRO brings a new level of interactivity to scatter chart analysis in Power BI. Users can seamlessly drill down through multiple hierarchy levels, zoom into dense data clusters, and pan across large datasets to investigate trends, outliers, and relationships with precision. The visual helps consumers report from high-level summaries to detailed insights without leaving the report. Advanced features such as dynamic regression lines, area and threshold highlighting, area selection, and lasso selection make it easier to identify patterns, compare segments, and focus on the most relevant data points. With over 200 formatting options, authors can tailor the visual to match business requirements and branding while creating highly engaging analytical experiences. Drill Down Scatter PRO is ideal for customer segmentation, sales performance analysis, operational monitoring, and any scenario where users need to explore large volumes of data interactively. By combining powerful analytics with intuitive navigation, the visual enables faster discovery of actionable insights directly within Power BI. To get started, download it now. Neutral variance color in Zebra BI Tables You're reviewing a Revenue vs Plan report. Anything within ±5% is considered fine, but Actuals are 3% below plan and everything flashes red. The whole dashboard looks alarming. Sound familiar? This is the fix. With Neutral variance color in Zebra BI Tables and Charts, you can now define a tolerance zone where small swings stay neutral. Variances beyond your set limits still turn red or green, so you instantly see where real action is needed. Where it helps: Revenue vs. Plan: "Set ±5% tolerance. Q4 revenue at -3%? Stays neutral. Drops to -7%? Turns red." Spend vs. Budget: Could we keep the current detail level but add context: "Department at 95% of budget? Neutral. Hits 115% or drops to 85%? Both turn red - overspend wastes money, underspend may signal delivery issues." On-time Delivery or HR KPIs: Could we pick one specific example - may be HR? Something like the below? Employee Turnover: "Annual target 12% ±2%. At 13.5%? Neutral. At 15%? Red alert." Now your dashboards reflect reality: not every variance needs a red alert. Instead, you can: Focus on what matters: Stop investigating variances within acceptable limits. Align with business rules: Your CFO says ±5% is fine, your dashboard should too. Achieve faster executive reviews: Leadership can scan dashboards and immediately spot true outliers. Try it now in Zebra BI Tables and Charts. Heatmap by Powerviz The Powerviz Heatmap visualizes data density and value distribution using color gradients, transforming complex datasets into an intuitive matrix format. It is ideal for identifying patterns, correlations, and outliers, aiding data-driven decision-making. Key Features: Shapes: Choose from Default, Fixed, or Diverging built-in shapes. Data Colors: Offers 30+ palettes, color-blind mode, and custom color options. Data Labels: Select from different styles available or add custom labels with formatting. Grid Lines: Improve data readability with visual grid lines. Reference Lines: Highlight key points using X-axis and Y-axis lines. Totals: Display row and column totals with advanced bar customization. Null Values: Customize null values (Blank /NA / Null/ ”0”) and their styling. Small Multiples: Split visuals into smaller ones using selected fields. Conditional Formatting: Spot outliers with Value/ Percentage/ Ranking based rules. Ranking: Filter Top/Bottom N rows or columns. Other features included Axis Settings, Grid View, Sorting, Show Condition and more. Resources Try Heatmap 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 Gantt by Profitbase Reviewing a project plan, stakeholders rely on color to quickly identify at-risk tasks, and owner-specific highlights. Version 3.1 of Gantt by Profitbase lets you apply conditional formatting rules independently to specific levels of your hierarchy. The new version also includes: Expand and collapse rows based on a condition, so the right level of detail opens automatically based on the rules added. A default scroll position at the start of the chart. Include the last day in duration calculations For more feature details, refer to the Gantt by Profitbase v3.1 release notes. Additional capabilities for Gantt by Profitbase: Unlimited hierarchy Format hierarchy levels individually Supports additional columns Supports multiple milestones per task Apply expansion styles Zoom in/out using zoom buttons Apply highlighting curtains to important periods Sort, Filter and Search for any information across all columns Use theming templates to set formatting Ragged Hierarchy Dynamic event markers from data Resources Version 3.1 Youtube video Gantt by Profitbase wiki Power BI Visuals YouTube Playlist Support Forum Learn more and download the visual on AppSource Until next month That’s a wrap for July! This month’s enhancements continue our focus on making Power BI easier to build, manage, and scale across your organization. From report-wide formatting improvements and expanded org app capabilities to new web modeling experiences and developer APIs, these updates help streamline both the authoring and consumption experience. We hope you enjoy trying out these new capabilities and, as always, we welcome your feedback as we continue to improve Power BI. Download Power BI Desktop
Introducing Fabric Lakehouse support in dbt job for Microsoft Fabric (Preview)
Fabric Lakehouse plays a central role in many Microsoft Fabric implementations, serving as the foundation where data is prepared, organized, and made ready for downstream analytics workloads. As more teams adopt dbt alongside Microsoft Fabric, a common goal is to bring transformation workflows closer to that same data foundation. With Fabric Lakehouse support in dbt job, you can run dbt projects directly against Fabric Lakehouse. Whether you're building transformation layers, managing business logic in dbt models, or standardizing analytics engineering practices, Fabric Lakehouse support makes it easier to incorporate dbt into Lakehouse-first architectures. Bring dbt workflows closer to your data Fabric Lakehouse support in dbt job helps teams: Build transformation layers directly on top of Fabric Lakehouse data. Apply dbt models and testing workflows as part of Lakehouse-based development. Align analytics engineering practices with Lakehouse-first architectures. Develop and manage transformation logic closer to where data is stored and prepared. Set up Fabric Lakehouse in dbt job Fabric Lakehouse support is built directly into the dbt configuration experience, making it easy to connect your project to an existing Lakehouse or create a new one as part of setup. The following steps will allow you to configure Fabric Lakehouse as the target for your dbt project: Open your dbt project. Navigate to dbt configurations. Select Lakehouse from the available adapters. Choose an existing Lakehouse from OneLake or create a new one. If creating a new Lakehouse: Select a workspace. Enter a Lakehouse name. Optionally enable Lakehouse schemas. Select Create and connect. Run your dbt project. A natural fit for Lakehouse-first architectures Many organizations use Fabric Lakehouse as the foundation for preparing and organizing data before it powers downstream analytics workloads. With Fabric Lakehouse support in dbt job, transformation workflows can run directly against that same foundation, helping teams manage business logic, testing, and model development using familiar dbt practices while continuing to build on Fabric Lakehouse. Ready to try it? Fabric Lakehouse support makes it easier to bring dbt workflows closer to where data is stored, prepared, and managed in Microsoft Fabric. To learn more about dbt job capabilities, supported adapters, runtime versions, and setup requirements, review the dbt job in Microsoft Fabric documentation. This documentation includes Fabric Lakehouse as a supported adapter. We'd love to hear how you're using Fabric Lakehouse support in dbt job, what scenarios it enables for your teams, and where you'd like to see us invest next. Please share your feedback, questions, and use cases in the comments below. Your input helps shape future investments in dbt job and ensures we're building capabilities that matter most to our customers.
Fabric Runtime 2.0 (Generally Available)
Fabric Runtime 2.0 (Generally Available), marks a major milestone for Microsoft Fabric. As the execution foundation for Microsoft Fabric's Data Engineering and Data Science experiences, Runtime 2.0 delivers a modern, high-performance platform built on Apache Spark and deeply integrated across the Fabric ecosystem.
Monitor and Explore your Stripe data in Power BI
Stripe is an advanced payment platform for online businesses. From start-ups to Fortune 500 companies, thousands of businesses use Stripe to accept payments in over 130 currencies, from anyone in the world. By connecting Power BI with your existing Stripe account, you’ll be able to use the Power BI Stripe content pack to monitor, explore, and visualize your Stripe activity. The content pack allows you to easily connect your Stripe data and begin to discover insights with the out-of-the box dashboard, reports, and curated data set on Power BI. This post will explain how the Power BI content pack can help you explore your Stripe data. For additional details on how to get started, please see the Power BI Stripe content pack help page. After you connect to the content pack using your Stripe credentials, your data will begin loading and a dashboard will be automatically created. From the out-of-box dashboard, you have the ability to select any of the tiles and further drill into the 4 reports including Events, Charges, Customers and Invoices. For example, if you select the Events visual, it brings you to the Events report. Once you are on a report you can hover over the visualizations to see additional details. This report highlights key event summaries and insights into the volume of the various events and their monetary impact. Reports are interactive, from this report you can select a specific currency from the currency slicer to filter the data for that specific currency. Switching into Edit Report shows all the underlying tables and fields available from the curated data set, allowing you further customize your reports and dashboards. From any of the reports you can pin the visualizations or tiles to the dashboard by hovering over it and selecting the pin icon. New tiles can be added or existing tiles moved to customize your dashboard. You can also try asking a question of your data, such as "total events by date" or "charges by charge type in a donut chart". Any of the results can also be pinned to your dashboard. After the initial import, the dashboard and the reports continue to update daily. You can control the refresh schedule on the dataset. With the Power BI Stripe content pack, you have an initial set of metrics and customer insights enabling you to explore your data even further. We’re always interested in hearing your feedback – please contact us at https://support.powerbi.com to let the team know how your experience was and if there’s anything we can do better. We look forward to your feedback! Try Power BI Follow @MSPowerBI on Twitter Visit the Power BI Community
Change Event Streaming for SQL database in Microsoft Fabric (Preview)
SQL database in Microsoft Fabric already brings operational and analytical workloads together—now it can stream changes as they happen. Change Event Streaming sends inserts, updates, and deletes directly to Fabric Eventstream or Azure Event Hubs. Build near-real-time pipelines from operational data without leaving Fabric.
Dataflows: Thank you for eight years of Gen1—and why Gen2 is the future
Updated on 20 April 2026: Thanks so much for all the engagement on this topic since the original publishing of this blog post on 2 April 2026. We’ve seen several recurring questions, and we’d like to clarify our position on a few key points. Dataflow Gen1 remains supported, but it is in a legacy state and won’t receive future innovation. We understand many customers have built important, business‑critical solutions on Gen1, and those existing workloads can continue to run while future investments are planned around Dataflow Gen2. Primary call to action for Premium customers: For Premium customers who already have access to Fabric, Dataflow Gen2 is the recommended path to take advantage of the latest improvements across functionality, productivity, performance, pricing, scale, reliability, and more. Many teams find it helpful to start by evaluating Gen2 for new or evolving workloads where these benefits can be realized quickly. Guidance for Pro and Premium Per User (PPU) customers: Many customers rely on Dataflow Gen1 in Pro/PPU today, and it can continue to be the right choice depending on the scenario. If Gen1 best fits your current use case, it remains supported and existing workloads can continue to run as-is. As we introduce new Dataflow Gen2 paths for Pro/PPU scenarios, we’ll share clear guidance and recommended steps to help with a smooth transition. GCC support for Premium customers: For customers using Dataflows Gen1 in GCC environments on Premium, Dataflow Gen2 support in GCC will be available before any future transition milestones, ensuring a supported upgrade path. More granular Fabric controls: Customers have asked for finer‑grained enablement than today’s all‑or‑nothing Fabric switch. Work is underway to provide more granular administrative controls, including the ability to enable only Dataflow Gen2, so capabilities can be rolled out incrementally with the right governance. Original post (2 April 2026): For more than eight years, customers have relied on Power BI Dataflows (Gen1) as a core part of their analytics solutions. We’re grateful for the trust you placed in the Power Query experience to build reusable, low-code data preparation pipelines that power reports, semantic models, and downstream analytics. Now, we’re sharing an update on the future of Dataflows. Dataflows Gen2 builds on everything you know from Gen1—preserving the familiar Power Query authoring experience—while delivering major improvements in scale, flexibility, cost efficiency, and manageability. Going forward, all new Dataflow innovation will land only in Dataflows Gen2. The future of Dataflows Gen1 Power BI Dataflows Gen1 has reached the end of active innovation and is moving into a Legacy state: Existing Gen1 dataflows will continue to work for the foreseeable future. However, specific retirement dates for Gen1 are being finalized, and we’ll share details as plans progress. For customers running Gen1 at Premium capacity, we will provide at least 12 months’ notice before Dataflow Gen1 is retired. (If you’re using Gen1 on Pro or Premium-Peruser, we still strongly recommend planning a move to Dataflows Gen2 to take advantage of the latest investments.) In the meantime, Gen1 artifacts will remain available and will be clearly marked as Legacy in product experiences, including the New Artifact menu. No new features are planned for Dataflows Gen1. Support will be limited to a narrow set of high impact issues, where changes can be delivered safely within the existing architecture. Many of the remaining Gen1 limitations would require significant architecture changes and are best addressed by moving to Dataflows Gen2, which was designed to solve these scenarios more comprehensively. The previous statements apply to all Dataflows Gen1—including usage on Pro, PPU, and Premium licenses. What’s new in Dataflows Gen2 Dataflows Gen2 retains the familiar Power Query experience while introducing substantial platform-level improvements to scale, performance, governance, and cost efficiency. The following updates provide additional details on each benefit area to help customers understand the full scope of Gen2 enhancements. More flexible destinations Dataflows Gen2 supports a significantly broader range of output destinations, enabling alignment with diverse data architectures across business, departmental, and enterprise scenarios. SharePoint and OneDrive are ideal for business users who need refreshed files (CSV, Excel) for downstream workflows, Office automation, or integrations with Power Automate and Teams. Azure Data Lake Storage is a great fit for customers building scalable ingestion pipelines for data science, machine learning, or lakehouse scenarios. Azure SQL Database / SQL MI enables operational reporting, standardized relational storage, and hybrid analytics scenarios. Microsoft Fabric Lakehouse / Warehouse / SQL analytics endpoints are the most seamless destination option for customers aligning with the Fabric vision. Gen2 integrates deeply with Fabric runtimes, unlocking better performance, more consistent semantics, and governance alignment. Snowflake and other cloud databases support multi‑cloud architectures and reduces friction for enterprise customers already standardized on multiple warehouse technologies. This broader range of destinations allows teams to use dataflows as a general-purpose low-code data ingestion and transformation layer, enabling many more scenarios never supported before with Dataflow Gen1. Improved performance and scale Dataflows Gen2 is built on the Fabric runtime and a modernized execution engine that delivers a step‑change in performance, reliability, and scalability. Modern query evaluation leverages Fabric's elastic compute layer to automatically manage scaling behavior, minimizing the need for manual optimization or workload tuning. Fast Copy technology supports high-throughput ingestion into Fabric destinations, enabling sustained data movement measured in gigabytes per minute. Parallelized execution enables multiple partitions and transformation steps to be processed concurrently, significantly reducing refresh durations compared to Gen1. Enhanced support for large datasets includes improvements in memory handling, high-cardinality data processing, and unbounded ingestion patterns. Predictable refresh behavior ensures more consistent performance under varying workload conditions. Together, these improvements establish a more durable and high-performance engine for data preparation, especially in enterprise environments that rely on large or frequently refreshed dataflows. Built-in AI assistance Dataflows Gen2 introduces integrated AI capabilities designed to accelerate development, improve quality, and reduce the learning curve for users working with complex transformations. Copilot-assisted authoring converts natural-language instructions into Power Query logic, improving productivity and lowering the barrier to entry for users with limited M expertise. Code explanation capabilities translate complex or legacy M scripts into easy-to-understand natural language descriptions, improving maintainability and simplifying onboarding. Automated performance and foldability recommendations help users align transformations to foldable patterns, resulting in faster load times and lower compute consumption. AI-powered data quality insights assist with identifying semantic types, outliers, and join keys and detecting common data issues early in the pipeline. These capabilities provide consistent guidance across the authoring lifecycle and support organizations with diverse skill levels. Richer diagnostics Gen2 introduces a more comprehensive set of diagnostics designed to improve traceability, troubleshooting, and operational reliability. Detailed refresh history now includes detailed timing information to clarify where time is spent during execution. Expanded logging and instrumentation provide visibility into foldability decisions, connector behaviors, authentication flows, and network operations. More consistent refresh semantics across all destinations ensures uniform behavior regardless of the target storage system. Greater operational transparency supports root-cause analysis and reduces the time required to identify and resolve failures. These diagnostic improvements help teams manage dataflows more efficiently and maintain higher levels of operational readiness. Tiered pricing and potential cost savings The introduction of a tiered pricing model is a major advantage for customers transitioning from Gen1. Gen2 decouples Dataflows execution from Premium capacity consumption, aligning compute usage with Fabric’s capacity unit (CU)–based architecture. This model allows customers to pay only for the compute resources required rather than maintaining always-on Premium capacity for workloads that may be intermittent or variable. Elastic scaling ensures that high-volume or burst workloads can consume proportionally more compute during peak times, while lighter workloads incur lower costs. Organizations with unpredictable or seasonal refresh patterns may see material cost reductions compared to Gen1 running on Premium capacity. This flexible cost model provides more predictable and efficient resource utilization, particularly for enterprises with diverse or rapidly evolving data refresh patterns. Our recommendation: Start planning your upgrade If you’re starting a new project, we strongly recommend using Dataflows Gen2. It offers better performance, richer diagnostics, built‑in AI, broader destination support, and a more flexible cost model. For existing Gen1 dataflows, now is the right time to begin planning your upgrade: Use Save as Dataflow Gen2 for quick, low effort upgrades of individual dataflows. For larger migration scenarios, the Save As API enables bulk migration and automation, supporting CI/CD workflows. For programmatic or large‑scale upgrades, refer to the Migrate to Dataflow Gen2 (CI/CD) guidance. Starting now allows you to modernize incrementally, beginning with a subset of your portfolio while keeping all your Power Query skills fully transferable. Thank you Thank you again for your long-standing investment in Dataflows and Power Query. We’re excited to support your transition to Dataflows Gen2 and to help you unlock new capabilities for the next generation of analytics solutions.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. rk support. 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!
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