Turning everyday documents from SharePoint and OneDrive into analytics ready data with OneLake shortcuts
We're making it easier than ever to bring the files your business lives in every day, such as Word documents, Excel workbooks, PowerPoint decks and PDF files, directly into your analytics in Microsoft OneLake. With OneDrive and SharePoint shortcuts, you can now reference your existing files in Microsoft 365 as if they were part of your OneLake storage, without copying or moving data. Your productivity content stays where it is, while your data engineers, analysts and AI workloads get a unified view over all of it inside Fabric. Why OneDrive and SharePoint shortcuts matter Most organizations already keep a huge amount of business-critical context in OneDrive and SharePoint. Sales decks and quarterly business review presentations. Contract PDF files and statements of work. Planning spreadsheets, forecasts and trackers. Project documentation, design specifications and meeting notes. Until now, bringing that content into your analytics workflows usually meant exporting, copying or syncing files into your data lake. This often creates silos, duplication and governance challenges. With OneDrive and SharePoint shortcuts in OneLake Your files stay in place in OneDrive or SharePoint. You can see them as part of OneLake storage, next to your existing structured and unstructured data. Governance is simplified with access enforced through Fabric and OneLake under a single security and management layer. This approach unlocks scenarios such as Joining Excel based forecasts with transactional data in your lakehouse. Indexing contract PDF files and project documents as part of your AI knowledge in Microsoft Foundry. Giving BI teams a single place to discover both operational tables and the documents that provide business context. From files to insights with AI powered transforms Shortcuts are a powerful way to virtualize your content into OneLake. Sometimes you want more than file access and need analytics ready data. The transform step in the shortcut creation flow lets you optionally apply transformations to your shortcut data before you finish. Use transforms to project your shortcut data into structured tables for analytics. Keep shortcut and transformed data in sync as new files arrive in your OneDrive or SharePoint folders. Combine shortcut-based transforms with the rest of your Fabric workloads, including warehouses, notebooks and Power BI. If you simply want quick access to your documents, you can skip the transform step and still benefit from unified access through OneLake. Get started To get started with OneDrive and SharePoint shortcuts in your lakehouse. Open a lakehouse in your Fabric workspace. Right click a folder and select new shortcut. Choose OneDrive or SharePoint Folder and connect with your organizational account. Select the folders that matter most for your scenarios such as sales decks, finance trackers and project documents. Browse OneDrive or SharePoint data From there you can use the same shortcuts to power reports, notebooks, AI agents and AI powered transforms, all while your files stay safely in OneDrive and SharePoint. To learn more, refer to the Create a OneDrive or SharePoint shortcut documentation.Simplifying secure data access with Delegated OneLake Shortcuts (Preview)
Introduction Data rarely stays in one place. As organizations standardize Microsoft Fabric and OneLake, the same datasets need to be reused across teams, domains, workspaces, and increasingly across tenant boundaries. The challenge is no longer moving data; it is sharing it securely, consistently, and at scale without creating copies, breaking governance, or forcing every consumer to be individually provisioned at the source. OneLake Shortcuts already solve a large part of this problem. A shortcut presents data where people need it while the data stays in its original location, enabling a true zero-copy approach to distribution. By default, OneLake Shortcuts use pass-through authentication: when a user reads a shortcut, Fabric accesses the target data using that signed-in user’s identity, and the data owner controls access directly on the target. Pass-through is the right model for many collaborative scenarios, but customers have consistently told us it does not fit every access pattern. Two points came up frequently: Access management does not scale. When a curated dataset must be served to thousands of downstream users across multiple teams, the data owner becomes responsible for granting and maintaining every individual user’s permission on the source, an operational bottleneck that grows with every new consumer. Cross-tenant sharing is harder than it should be. Multi-tenant organizations told us that they need to access data residing in OneLake across their own tenant. These are not edge cases. They are everyday realities for enterprises building governed, reusable data products on Fabric. The preview of Delegated OneLake Shortcuts — including delegated sharing both within a tenant and across tenants — gives data owners a simpler, governed way to distribute data without compromising on security. Introducing delegated OneLake Shortcuts Delegated OneLake Shortcuts add a second authentication option to the existing shortcut experience you already know. Instead of accessing the target data as each signed-in user, a delegated shortcut accesses the target through a configured connection identity. That identity can be an organizational account, a service principal. This identity is attached to the shortcut, so all access to the shortcut reaches the target as the delegated identity. Delegated authentication is entirely optional and complements the default experience. If a user does not choose delegated authentication when creating a shortcut, the shortcut continues to use pass-through authentication exactly as before. The default flow is unchanged; delegation is simply there when you need it. How it works A delegated shortcut behaves like other external shortcuts in Fabric. When you create one, you sp, and that connection is used to browse and read the target data. This brings a familiar, governed connection model to OneLake-to-OneLake sharing. Identity delegation - Downstream users access the data through the delegated identity rather than their own, so the data owner no longer must provision each individual consumer on the source item. Secure access enforcement with OneLake security - OneLake security roles can be configured on both the data producer and data consumer delegated Shortcuts. At the time of this writing, table level security and column-level security are supported for delegated shortcuts, on both the target (where you are creating the shortcut) and the shortcut source (where data resides). Delegated permissions management - A shortcut can delegate as a fixed identity that represents a business unit. The central data owner controls what that identity can see, while the business unit manages OneLake security for its own end users, all while still honoring the controls applied to the delegated identity. Cross-tenant sharing Delegated shortcuts also work across Microsoft Fabric tenants. A cross-tenant delegated shortcut lets you create a OneLake shortcut to data that lives in another organization’s Fabric tenant. You provide a connection path to the external OneLake data and authenticate with an identity from that tenant; downstream users then access the external data through the configured delegated identity, without each user needing individual cross-tenant permissions. This makes delegated shortcuts a natural fit for multi-tenant enterprises, for example, sharing curated data between an organization’s test and production tenants, or between a parent company and a subsidiary using the same zero-copy, intersection-based security model that applies within a tenant. Difference between External Data Sharing and delegated Shortcuts Microsoft Fabric has External Data Sharing, a feature that enables Fabric users to share data from their tenant with users in another Fabric tenant. External data sharing can be used when the consumer has no identity in the producer's tenant, such as sharing across organizational boundaries with an outside partner or customer. This is ideal when you must share with partners or when ISVs must share data with their customers and don’t want to have the consumer identity in their tenant. Cross-tenant delegated shortcuts are used when the data consumer has an identity, such as an organizational account or service principal, in the producer’s tenant. For example, an organization can share data between its own test and production tenants, with access flowing through the configured delegated identity. Use cases Delegated OneLake Shortcuts are designed for the moments when the default pass-through behavior does not match the access pattern you want for a data product. Common scenarios include: Departmental data sharing at scale - Represent each department with a delegated identity, scope what that identity can see, and let department owners manage access for their own users instead of routing every request through the central data owner. Cross-tenant and subsidiary sharing - Share curated data between tenants — such as test-to-production or parent-to-subsidiary — with no data copies and the same delegated security model. Getting started Open the target Fabric item, such as a Lakehouse, and select Get data > New table shortcut. In New shortcut, select Microsoft OneLake, then choose the source you want to shortcut to. For cross-tenant data, select Enter connection details and provide the external OneLake path. For Connection method, select Delegated identity, then Connect. Choose an existing connection or create a new one by providing the OneLake path, a recognizable connection name, and an authentication kind (organizational account or service principal). Sign in to complete authentication. Browse the source, select the folders or tables to include, then review and create the shortcut. To switch an existing shortcut between pass-through and delegated authentication, delete and recreate it with the desired method. For detailed steps, refer to the OneLake Shortcuts documentation. Conclusion and next steps OneLake shortcuts are a foundational building block for zero-copy data distribution across Microsoft Fabric. Delegated OneLake Shortcuts extend that foundation to the scenarios enterprises care about most: serving curated data to large audiences, delegating access management to the teams closest to the users, and sharing securely across tenant boundaries. Together, pass-through and delegated shortcuts let organizations choose the right balance of control, scale, and simplicity for each data product. Pass-through keeps source-managed authorization per person for collaborative engineering. Delegated mode turns a shortcut into part of a governed publishing architecture: central teams retain ownership of the source, consuming teams avoid copying data, and downstream audiences access a managed experience rather than raw-path access — without ever giving up the governance benefits of unifying data in OneLake. Share your feedback, use cases, and questions in the Microsoft Fabric Community. Your input directly shapes the roadmap.SharePoint and OneDrive Shortcuts in OneLake (Generally Available)
For most enterprises, the largest and fastest-growing repository of business-critical information is SharePoint and OneDrive. Contracts, financial models, project plans, meeting notes, presentations, and compliance documentation accumulate across every team and department, rich with context that rarely makes it into an analytics workflow. This content has traditionally been invisible to data platforms. Bringing it into a data lake meant building pipelines, scheduling exports, managing duplicates, and reconciling governance across two separate systems. The result: valuable knowledge stayed locked in productivity tools while analytics teams worked with an incomplete picture. Now, SharePoint and OneDrive Shortcuts in Microsoft Fabric OneLake is now generally available. With this capability, organizations can now surface this data directly inside OneLake, without requiring traditional data movement pipelines in many common scenarios. Files stay exactly where they are in SharePoint or OneDrive, and Fabric workloads see them as a native part of the data lake. This is more than a connectivity feature. It is the bridge between the world where work happens and the world where data is analyzed. Sales forecasts stored in Excel can be joined with CRM transactions. Legal documents can be indexed and grounded in AI agents. Financial trackers can feed Power BI reports the moment they are updated. This helps reduce the boundary between collaboration content and enterprise analytics workflows. Customer Use Cases Organizations across industries are already finding practical, high-value ways to connect their Microsoft 365 content with Fabric analytics and AI. Below are example scenarios that illustrate potential use cases. Data Lake Unification Without Migration Many organizations maintain structured data in their Lakehouse alongside a parallel, untouched archive of unstructured content in SharePoint. Shortcuts eliminate the need to choose between the two. Data engineering teams can now create a unified view across both sources inside OneLake, enabling joins, aggregations, and AI workflows that span the full breadth of enterprise knowledge, without moving a single file. Combine SharePoint-hosted reference tables with Lakehouse transactional data in a single Spark notebook. Surface operational documents alongside structured metrics in a single Power BI semantic model. Avoid costly migration projects by referencing content in place and retiring redundant sync processes. Self-Service Analytics on Shared Documents Finance, HR, and operations teams frequently maintain planning workbooks, trackers, and reports in OneDrive and SharePoint. These files are updated regularly by business users who have no need or desire to interact with a data platform directly. Shortcuts let analysts consume this content without asking anyone to change how they work. Finance teams can surface quarterly budget workbooks directly in Power BI without any export step. HR can connect headcount trackers and org charts to workforce analytics dashboards. Operations teams can make procurement logs and vendor documents queryable alongside ERP data. Microsoft Fabric and Foundry: AI at Enterprise Scale For organizations building production-grade AI solutions on Azure, the combination of Fabric OneLake and Microsoft Foundry creates a powerful foundation. Shortcuts ensure that the rich, unstructured knowledge stored in SharePoint and OneDrive is available as a live, governed data source for Foundry-based agents and copilots. Connect SharePoint document libraries to Foundry knowledge stores without a separate ingestion pipeline. Keep AI knowledge grounding current automatically as SharePoint content is updated by business teams. Apply Fabric data transformations to prepare document content for structured AI consumption at scale. Features Supported in General Availability The following capabilities are generally available and supported for production use as of this release: Core Shortcut Capabilities Create shortcuts from any Fabric Lakehouse directly to OneDrive folders or SharePoint document libraries. Access files in place, without requiring explicit data duplication in many scenarios. Files remain governed by their existing SharePoint and OneDrive permissions. Live synchronization ensures that as content is added or updated in SharePoint or OneDrive, Fabric workloads see the latest version automatically. Shortcut Transformations Beyond simple file access, OneLake shortcuts include an optional transformation step that converts supported file types directly into Delta tables. This can reduce or eliminate the need for a separate ETL pipeline in supported scenarios, making document-resident data queryable by analytics engines. Supported file formats for transformation at GA: CSV, Parquet, and JSON. Transformed tables are kept in sync as new files arrive in the connected SharePoint or OneDrive folder. Transformed output integrates natively with Fabric warehouses, notebooks, and Power BI, enabling immediate analytics without additional data engineering. This capability fundamentally changes how customers are working with file-based data. A SharePoint folder containing hundreds of CSV exports from a business application can be transformed into a queryable Delta table in minutes, with minimal pipeline code required in most scenarios. General Availability Improvements In addition to the core shortcut capability, this GA release introduces two significant platform-level improvements that make shortcuts enterprise-ready for automation, scale, and cross-organizational scenarios. Service Principal and Workspace Identity Authentication OneDrive and SharePoint Shortcuts now support Service Principal (SPN) and Workspace Identity (WI) authentication, in addition to organizational account sign-in. This is a critical capability for production deployments. Reduces dependency on individual user credentials, preventing pipeline failures when team members change roles or leave the organization. Authentication is managed through Microsoft Entra ID, enabling consistent security governance and audit trails. SPN and WI authentication support higher API limits, directly reducing throttling in high-throughput scenarios. Cross-tenant access: service principals can be configured to access SharePoint and OneDrive content across organizational boundaries, enabling configurable partner and subsidiary data sharing scenarios, subject to tenant policies and security configurations. Metadata Caching and Performance OneLake now caches SharePoint metadata internally, reducing the frequency and impact of calls to the SharePoint API during query execution. This improvement can deliver the following benefits: Reduced throttling under high query volumes, particularly in multi-user or scheduled workload scenarios. Improved query performance for workloads that enumerate or filter large SharePoint folder structures. These improvements make shortcuts viable for production-grade pipelines and scheduled refresh scenarios that would previously have encountered reliability issues at scale. How to Get Started Creating a SharePoint or OneDrive shortcut in Fabric takes less than five minutes. The following steps apply to any Fabric workspace with at least one Lakehouse: Open a Lakehouse in your Fabric workspace. In the Explorer pane, right-click any folder and select New shortcut. In the New Shortcut dialog, select OneDrive or SharePoint tile from the list of external sources. Choose your authentication method: Organizational account for interactive scenarios, or Workspace Identity / Service Principal for automated and production workflows. Provide the SharePoint site URL and select or create a connection. If you don’t have root level access and prefer to provide the path directly, change the view to Path View by navigating to the top right corner. Browse to the folder or library you want to connect. Select one or more target locations and select Next. On the Transform page, choose whether to apply a transformation to convert supported file types (CSV, Parquet, JSON) into Delta tables. Select Skip if you only need file access. Select Create to finalize. Your shortcuts will appear immediately in the Lakehouse Explorer. From there, you can reference them in Spark notebooks, build Power BI reports directly on the data, run SQL queries through the Lakehouse SQL endpoint, or include them in Fabric pipelines and AI workflows. Learn more by exploring Create a OneDrive or SharePoint shortcut (Microsoft Learn documentation). We are excited to see what you build SharePoint and OneDrive Shortcuts in OneLake represent a step toward a world where every document in your organization is an active participant in your data and AI strategy. As the boundary between productivity and analytics continues to dissolve, Fabric is designed to be the platform that connects them. Share your feedback, use cases, and questions in the Microsoft Fabric Community forums. Your input directly shapes the roadmap.
Copy job for SAP with ABAP Add-On in Microsoft Fabric (Preview)
SAP systems sit at the center of many enterprises’ core business operations, powering processes across finance, supply chain, manufacturing, procurement, and HR. That makes SAP data some of the most business-critical data in the enterprise. As organizations modernize their analytics and AI platforms, bringing SAP data together with the rest of the enterprise data estate has become increasingly important. But moving SAP data at enterprise scale has historically been difficult. SAP landscapes are complex, data volumes are large, and extraction architectures often require specialized frameworks, custom code, and additional operational layers. For many organizations, this creates friction between where their most important operational data lives and where they want to analyze, enrich, and activate it. We are delivering the next step of our SAP roadmap: Copy job for SAP with ABAP Add-On in Microsoft, this new capability complements offerings like: SAP Business Data Cloud Connect was announced in SAP and Microsoft accelerate business insights and AI innovation with SAP Business Data Cloud Connect for Microsoft Fabric and will be available over the coming months). It will enable bi-directional zero-copy sharing between SAP Business Data Cloud and Microsoft Fabric without the need for data movement. Mirroring via SAP Datasphere, a turnkey data replication capability providing scalable near real-time data movement from SAP sources into Fabric OneLake (Generally Available). To learn more, refer to the Fabric mirroring documentation. Now, organizations can extract large volumes of SAP data through Copy job, reduce the need for external extraction frameworks, and build a scalable path from initial ingestion to ongoing incremental updates. Copy job provides a configuration driven experience for moving data across clouds, applications and on-premises systems – designed to support high-scale, multi-cloud data movement for petabyte-scale ingestion scenarios. Copy job helps bring data into OneLake as part of a unified, governed foundation for analytics and AI. High-performance SAP extraction with Copy job and Microsoft ABAP add-on integration This new integration option enables scalable, high-throughput extraction from SAP systems, making it easier than ever to bring large volumes of SAP data into Microsoft Fabric for analytics, reporting, and AI. Figure: High-level architecture diagram of Copy job with ABAP Add-on. Broad data coverage across SAP Systems Extracting data from SAP systems at scale can be complex and resource intensive. This new capability simplifies the process while delivering high performance. The solution runs directly inside SAP using a Microsoft-provided ABAP Add-On. Data is extracted at the application layer and transferred efficiently through Copy Job in Fabric Data Factory. This removes the need for complex external extraction frameworks and reduces operational overhead. It supports a wide range of SAP data sources. You can extract from SAP tables, views, and ABAP CDS views, including semantically rich models used for analytics. The capability works across both SAP ECC and SAP S/4HANA, whether deployed on-premises or in the cloud. Providing consistent access to both raw operational data and business-ready data models. Flexible data delivery styles for real-world SAP scenarios Through the integration with Copy Job, the solution handles data movement at scale. The integration is designed for high throughput and efficient processing of large datasets. Parallel execution capabilities help accelerate ingestion and reduce data transfer times. The solution supports different loading strategies depending on your needs. You can perform full snapshots for initial ingestion or bulk replication scenarios. For ongoing updates, incremental loads can be configured using watermark-based extraction. This allows you to process only the data that has changed. Together, these capabilities make it easier to design efficient pipelines. You can start with a large initial load and then transition seamlessly to incremental updates, without changing the overall architecture. SAP data in OneLake, ready for analytics and AI After ingestion, SAP data is immediately available in Microsoft OneLake. From there, it can be used across the entire Fabric platform. You can build Power BI reports directly on SAP data and operational datasets. You can run large-scale analytics workloads or combine SAP data with non-SAP sources from across your organization creating a unified data foundation without silos. With all data in one place, it also becomes easier to enable AI-driven scenarios. Applications and data agents can access consistent, trusted business data to generate insights and drive automation. Get started For more information, refer to the ABAP Add-On for SAP data extraction with Copy Job in Microsoft Fabric documentation. You can start exploring this capability today to: Accelerate SAP data ingestion Simplify data extraction architectures Enable scalable analytics and AI scenarios Looking ahead Copy job for SAP with Microsoft ABAP Add-On is another step in our continued investment in SAP data integration for Microsoft Fabric. SAP is one of the most important enterprise data sources for our customers, and Data Factory in Fabric is designed to help organizations bring SAP and non-SAP data together through scalable, flexible data movement experiences. With Copy job, customers can move data across clouds, applications, and on-premises systems into OneLake. With the Microsoft provided ABAP Add-On, that same Copy job experience now extends more deeply into SAP, helping customers bring business-critical SAP data into Fabric for analytics, reporting, and AI. We will continue expanding SAP data integration capabilities across Microsoft Fabric so customers can simplify their data architectures, reduce silos, and build a unified, governed foundation for enterprise analytics and AI.
Enabling Item Recovery by default in Microsoft Fabric
If you’ve ever had someone delete the wrong item by accident, this update is for you. Starting August 16, 2026, Fabric will be rolling out enabling Item Recovery by default for all tenants that have not set it explicitly. That means supported item types get a built-in recovery window without extra setup. The goal is simple: make accidental deletion less painful while keeping tenant admins in control. You can still disable Item Recovery or choose a different retention value based on your policy. Nothing in this change removes admin choice. Brief summary Changes on August 16, 2026 include: Tenants with no explicit Item Recovery setting move to enabled by default. The default retention window for supported item types is three days. Existing explicit settings are preserved. New tenants created on or after the date start with Item Recovery enabled. The following does not change: If you already enabled Item Recovery, your current retention value stays the same. If you explicitly disabled Item Recovery, it stays disabled. You can still set retention between 3 and 90 days or disable the feature. Why this helps in real operations Accidental deletes happen in normal work, especially in environments with automation, shared workspaces, and fast release cycles. Without a recovery window, a simple mistake can become a support escalation. With default enablement, teams have a predictable way to restore supported deleted items quickly. This is most useful when speed matters. Support teams can follow a known restore path, and admins do not need to scramble to enable protection after an incident. It also gives tenants a consistent baseline instead of relying on whether someone remembered to turn the setting on. What users will experience after deletion When a user deletes a supported item, it goes into a soft-deleted state. During retention, authorized users can restore it through existing product surfaces. The restore flow is familiar and does not require a new tool. The following animation shows a supported deleted item appearing in Workspace Recycle bin and then being restored. This is the most common recovery path teams will use in day-to-day incidents. After retention expires, the item is hard-deleted and can no longer be restored through Item Recovery. This is the key boundary support teams should document in incident runbooks. For full product behavior, refer to Retention and recovery in Microsoft Fabric documentation. What admins should do before rollout The default applies automatically for tenants without an explicit setting, but a short prep pass helps avoid confusion later. Focus on policy fit, support readiness, and billing expectations. Checklist: Open Fabric admin portal and review Tenant settings > Item Recovery. Confirm whether the 3-day default fits your policy. If needed, set a different retention value or disable Item Recovery. Update support and on-call runbooks with restore steps for supported item types. Confirm who in each workspace can restore and who should escalate. Share a short rollout note with admins and support teams. Impact on billing Item Recovery does not introduce a new meter. Soft-deleted supported items are billed through standard OneLake storage while they remain retained. For storage details, refer to OneLake consumption documentation. Common scenarios this prevents from becoming incidents Default enablement is helpful in common situations where people move fast and mistakes happen: A contributor removes the wrong item during cleanup. A deployment script deletes an item during rollback. An engineer removes a dependent item while troubleshooting. A team catches yesterday’s deletion and restores within retention. In all cases, the value is the same: teams get a practical window to recover without jumping straight to complex recovery paths. Frequently asked questions Will this override settings my admin team already chose? No. If your tenant already has an explicit Item Recovery setting, that value stays in place. Do we need to do anything to get the default? No action is required for tenants that are currently unset. The default applies automatically on the effective date. Can we turn it off after rollout? Yes. Tenant admins can disable Item Recovery at any time in Tenant settings. Can we use a longer retention window than 3 days? Yes. Tenant admins can select a value from 3 to 90 days based on policy and risk tolerance. Does this mean all Fabric item types are recoverable? No. The behavior applies to supported item types. If you are unsure whether an item type is supported, check the product documentation linked below. Additional references Retention and recovery in Microsoft Fabric documentation OneLake consumption documentation Item recovery in Microsoft Fabric (Preview) Call to action Before August 16, 2026, open Fabric admin portal and review Tenant settings > Item Recovery. Confirm that retention matches your policy, update support runbooks, and brief workspace owners on the restore path for supported item types. After rollout, monitor OneLake storage trends and adjust retention if needed.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!
Bring your Azure Monitor and AWS Glue data to OneLake! (Preview)
Microsoft Fabric is expanding the reach of Microsoft OneLake with two new mirroring capabilities: Mirrored Azure Monitor and Mirrored AWS Glue catalog. These previews make it easier to bring operational telemetry and tables from external catalogs into Fabric while minimizing data movement and integration complexity. Bring more of your data estate into OneLake Organizations manage data across clouds and platforms. As two examples, operational telemetry may live in Azure Monitor, and some lakehouse environments may use AWS Glue Data Catalog to organize Apache Iceberg tables stored in Amazon S3. Bringing these systems together traditionally requires custom ingestion pipelines, duplicate storage, and ongoing infrastructure management. Fabric mirroring provides a simpler model: it connects to external systems and reflects data or metadata into OneLake and Fabric experiences, helping teams analyze information across platform boundaries. Mirror your Azure Monitor data into Fabric Mirrored Azure Monitor brings tables from Log Analytics workspaces into OneLake without duplicating the operational data. It connects OneLake to the Log Analytics storage used by Azure Monitor, allowing teams to combine telemetry with business data already available in OneLake. This creates opportunities for: Operational analytics across application and infrastructure signals. Cross-domain reporting that connects service health with business outcomes. Real-time intelligence scenarios using KQL. AI-powered reasoning across operational and business data. Because the data remains governed by Azure Monitor and is accessed without replication, teams can avoid building a second ingestion and storage path merely to analyze telemetry in Fabric. Bring Iceberg tables from AWS Glue into OneLake Mirrored AWS Glue catalog is designed for organizations that use AWS Glue Data Catalog for their Apache Iceberg tables backed by AWS S3. Users connect OneLake to AWS Glue, select supported Iceberg tables, and have their Iceberg tables automatically show up in OneLake for use in Fabric. The experience is intended to enable: Unified discovery of Glue-cataloged Iceberg data with other OneLake data. Faster onboarding of cataloged data into Fabric analytics experiences. Cross-cloud analysis using Fabric workloads such as Power BI, Data Warehousing, Data Engineering, and Data Science. The underlying data remains in its existing storage location, while Fabric uses mirrored metadata and OneLake shortcuts to make supported Iceberg tables available across the platform. Open by design These capabilities continue Microsoft’s commitment to open data architectures and cross-platform interoperability. The mirrored catalog feature establishes a metadata-based, zero-copy pattern for bringing external catalog-managed data into OneLake. Mirrored AWS Glue catalog and Mirrored Azure Monitor extends the mirrored catalog approach to additional providers and environments. Get started Refer to the following steps to try out mirroring for these new sources today! Mirrored Azure Monitor To set up mirroring for your Azure Monitor tables from your Log Analytics workspace: Create a new Mirrored Azure Monitor item in Fabric. Connect it to a supported Log Analytics workspace. Select the tables you want to make available. Start exploring and analyzing the mirrored tables using Fabric experiences. Check out the Mirror Azure Monitor in Microsoft Fabric (preview) documentation for more guidance. Mirrored AWS Glue catalog To set up mirroring for Iceberg tables from your AWS Glue catalog: Create a new Mirrored AWS Glue catalog item in Fabric. Connect to AWS Glue Data Catalog using a supported authentication method. Select the Iceberg tables you’d like to mirror into OneLake. Start exploring and analyzing the mirrored tables using Fabric experiences. Check out the Mirrored AWS Glue catalog (preview) documentation for more information. Mirrored Azure Monitor and Mirrored AWS Glue catalog help bring more of your data estate into OneLake, reducing the complexity of traditional integration projects and making it easier to work across operational, business, and cross-cloud data. We want your feedback! Try the previews today! Share your feedback through the Fabric Ideas site and Microsoft Fabric Community!Microsoft recognized as a Leader in The Forrester Wave™: Data Lakehouses
For years, organizations have invested in data platforms to understand what happened across their business. Dashboards, reports, and KPIs are now table stakes. But as AI becomes central to how organizations operate, the bar for the data lakehouse is getting much higher. The next generation of applications and agents needs governed access to every kind of data: structured, semi-structured, and unstructured; batch, streaming, and real-time—working together on one open foundation. When that data remains spread across fragmented systems, teams are left reconciling copies, duplicating governance, and stitching together context before they can create value. Today, we're proud to share that Microsoft has been recognized as a Leader in The Forrester Wave™: Data Lakehouses, Q3 2026. In the report, Forrester describes Microsoft Fabric as “a strong fit for enterprises seeking a unified, AI-enabled lakehouse platform integrated with the Microsoft ecosystem.” We believe this recognition reflects the bold vision behind Microsoft Fabric and Microsoft OneLake: helping organizations eliminate the integration tax of fragmented data estates by bringing data, analytics, governance, and AI together on one open lakehouse foundation. Fabric: A unified foundation for the AI-era lakehouse The Forrester report frames the modern lakehouse as more than a system of record for analytics. As agentic AI systems begin to reason, plan, and act on enterprise data, the lakehouse is becoming the operational foundation where intelligence is grounded and activated in real time. Microsoft Fabric was built for this shift. With OneLake, Fabric gives organizations a single, governed data lake and one SaaS platform where data teams, analysts, developers, and business users can work from the same trusted foundation. Forrester notes that Microsoft’s approach emphasizes deep integration across Power BI, Copilot, Microsoft 365, OneLake, databases, and AI services—helping unify analytics, operational, and AI workloads within a single ecosystem. Forrester also highlights Microsoft’s “bold vision of a unified, AI-powered, open data platform that brings together analytics, data engineering, business intelligence, and operational data.” Furthermore, they add that “innovations such as OneLake shortcuts, mirroring, AI-powered transformations, and cross-cloud interoperability support this vision by reducing silos and simplifying access to distributed data.” One open foundation for data and analytics OneLake is the governed data lake at the heart of Fabric, designed to unify your entire multi‑cloud data estate. It connects data across clouds and on‑premises systems using zero‑copy, zero‑ETL access, so teams work from a single, governed copy of data. With native support for open formats like Delta Lake and Iceberg, this data remains accessible from any analytics engine or platform, including Microsoft Fabric, Snowflake, and Azure Databricks. Once data is connected or stored in OneLake, the OneLake catalog helps secure, govern, and organize it into a logical data mesh, making trusted data easy for everyone to discover and use. Govern once, across every engine Security, identity, lineage, and governance are built into Fabric rather than bolted on tool by tool. State of the art OneLake security can define object-, row-, and column-level controls once and enforce them consistently across Spark, SQL, KQL, Power BI, Copilot, and third-party engines through OneLake security APIs. The OneLake catalog centralizes sensitivity labels, classification, and end-to-end lineage, helping organizations simplify governance while giving users trusted access to the data they need. Every workload on one lakehouse Fabric brings relational, real-time, analytical, document, and vector workloads into one platform experience on OneLake. Spark powers data engineering with the Native Spark Execution Engine in Microsoft Fabric, which accelerates workloads by running much of the execution in highly optimized native C++ code with vectorized processing, while preserving the same Spark APIs, notebooks, and DataFrame code users already know. With the native execution engine, Spark in Fabric delivers up to 6x faster performance than open-source Apache Spark, helping improve price performance by completing the same workloads with less compute and lower costs. Additionally, the warehouse engine and lakehouse SQL endpoint serve interactive queries over the same Delta tables; Real-Time Intelligence supports streaming and event-driven scenarios; and Power BI queries OneLake directly through Direct Lake without importing or moving data. AI native to the data platform Fabric’s integration with Copilot and agents enable natural language analytics and intelligent automation at scale. Vector embeddings can sit alongside structured data for AI retrieval, so teams can build analytics, AI, and applications on one governed copy of data. Instead of moving data to each workload, organizations can bring more workloads and more AI-powered experiences to the same open lakehouse. Why organizations choose Fabric Customers are seeing real impact from using Fabric: less duplication, cleaner governance, faster development, and a simpler path from data to AI. At the foundation is a common pattern: organizations are consolidating fragmented data estates onto a single governed lakehouse. London Stock Exchange Group (LSEG), a partner to the world's leading financial institutions, set out to simplify a complex, fragmented data landscape and give its customers a consistent, unified experience. Using Fabric, LSEG is building a unified data platform that consolidated 30 systems, 1,200 datasets, and 33 petabytes of data, accelerating product development, improving data quality, and advancing AI readiness. Product development timelines have moved from years to months, delivering faster, cleaner data to everyone from global firms to individual traders. "When you need to pull data together across disparate sources that are in different formats and varying levels of modernisation and maturity, it makes it difficult to react to market demand quickly. We knew it would be far more efficient to bring everything into a single, modern platform. It would mean we could run the organisation leaner and react to market demand faster.” Dave Byrne, Group Head of Data Platforms at LSEG Once data is unified, organizations can apply governance and analytics at enterprise scale. UNC Health standardized its enterprise data estate on Fabric, creating a single, governed lakehouse foundation for clinical analytics, operations, population health, and research. Fabric powers UNC Health’s AI solutions, which reduced care-gap chart review time by nearly 50% and provides the governed data foundation for a secure research environment supporting 25 active studies. That same foundation also creates new opportunities for AI-driven innovation. Eastman, a global specialty materials company, adopted Fabric to modernize its legacy data architecture and create a unified, governed lakehouse foundation for analytics and AI. Using OneLake shortcuts and data mirroring, Eastman shares data across domains without unnecessary duplication, ingested roughly one billion rows from eight systems, and established a scalable platform for analytics, machine learning, and AI-powered applications. “We operate in a lot of markets that are fundamentally different from one another. Being able to aggregate all this loose, unstructured data into something that’s actionable is really helping our commercial organization build better strategies for the year ahead.” —Andrew Ervin, Manager of Generative AI, Eastman Taken together, these examples illustrate the evolution of the modern lakehouse: first unifying data, then governing it consistently, and ultimately turning it into a foundation for AI-powered innovation. This is the shift that many organizations are making as they prepare for the next generation of applications and agents. Strategic takeaways for enterprise leaders Three shifts stand out for leaders preparing their organizations for the next generation of AI: The lakehouse is now the default foundation for AI. Agents and AI applications need governed access across every data type; not siloed systems stitched together after the fact. Openness prevents lock-in. Open table formats and bi-directional interoperability help organizations unify their estate without walking away from the tools and platforms they already run. Reducing duplication changes the economics. Bringing workloads to a shared, governed foundation helps organizations simplify operations, strengthen consistency, and accelerate innovation. As organizations prepare for the next generation of AI-powered applications and agents, the need for a unified, open, and governed data foundation will only grow. Microsoft Fabric was built to bring data, analytics, governance, and AI together on that foundation, and we’re excited to keep innovating alongside our customers. Forrester’s recognition reinforces what we’ve believed from the start: the organizations that succeed in the AI era will be those that eliminate fragmentation, simplify governance, and build on one open lakehouse. Learn more Read the complimentary report. Explore Microsoft OneLake and Microsoft’s vision of an open data lake ecosystem. Join us at the next Fabric + SQL Community Conference in Barcelona to hear directly from our product and teams and community members. Statement from Forrester Forrester does not endorse any company, product, brand, or service included in its research publications and does not advise any person to select the products or services of any company or brand based on the ratings included in such publications. Information is based on the best available resources. Opinions reflect judgment at the time and are subject to change. This report is part of a broader collection of Forrester resources, including interactive models, frameworks, tools, data, and access to analyst guidance. For more information, read about Forrester's objectivity here.
