Advanced NL2SQL and advanced DAX generation in Fabric data agents (Generally Available)
Data agents in Microsoft Fabric let users ask questions about data in plain language. As a creator, you connect an agent to supported sources—including SQL databases, lakehouses, Power BI semantic models, and eventhouses—add the business context it needs, and share it with users across Fabric, Teams, Microsoft 365 Copilot, Copilot Studio, and Microsoft Foundry. The data agent can then respond like a domain expert that understands what your tables mean, how your metrics are defined, and which source can answer a question. The usefulness of a data agent ultimately depends on a simple question: can it translate what a user asks into the right query? That is harder than it sounds. Enterprise schemas are rarely intuitive. Several tables, columns, measures, or join paths may appear to answer the same question, so a query can be technically valid but still return the wrong result. Advanced NL2SQL and advanced DAX generation help address this problem by allowing the agent to explore the schema and data, validate assumptions, and refine its approach before generating a query. Both capabilities are available today, and we'll begin rolling the core improvements out to the standard runtime on November 2, with the rollout expected to complete by November 10. In this post, we'll look at how they work, what our internal benchmarks show, the new configurations available for shaping how the agent behaves, and what to consider as you re-test and evaluate your data agents. Why conversational analytics gets harder with enterprise data Turning a plain-language question into a correct query is a complicated problem, both for SQL and DAX, requiring the agent to resolve several challenges at once: Schemas are large. Enterprise sources can contain hundreds of tables, columns, and measures, and the names alone may not explain when or how they should be used. Business context exists outside the data. Definitions such as “revenue” or “active customer” often live in documentation, previous queries, or analysts’ knowledge rather than in the source schema. Filter values must match the way data is stored. Users describe concepts naturally, but those concepts must map to the values in the data. For example, a user may ask about “California” when the relevant column stores the value “CA.” Questions are ambiguous. More than one column, calculation, or join may appear to satisfy a request. Individually, each of these can be managed with enough hand-written guidance. The difficulty is that they compound: a single-pass generator has to resolve all of them at once — your schema, your definitions, your filter values — committing to an interpretation before it has any evidence that interpretation is right. When it guesses wrong, the query still runs, and the answer looks no different from a correct one. Advanced NL2SQL and advanced DAX generation are built around that problem. Rather than generating a single pass, the agent gathers evidence first. Advanced NL2SQL: the agent explores before it writes The shift is architectural. Instead of one-shot generation, the agent has sub-tools to explore schema, test joins, and look up real filter values before it writes SQL. Under the hood, you can inspect each step the agent took, and the assumptions and reasoning behind it. It operates in three modes, each backed by its own sub-tools: Mode What it does Example query following Finds a relevant example query and answers by making approved modifications, such as literal filter or parameter substitutions. Clarifying questions Determines it can’t answer reliably without more information and asks targeted clarifying questions before generating SQL. Data exploration When no example directly applies, learns from existing example queries to infer business logic, explores the data to validate assumptions, and iteratively refines its approach. What you get as a creator: Advanced NL2SQL explores the SQL source schema, tests joins, and looks up real filter values before generating SQL, helping it choose the appropriate tables, columns, relationships, and filters. Schema descriptions and topics that teach the agent what your data means, so you get better answers with fewer instructions. Better example-following and filter-value substitution for novel questions. Handles large schemas far better and runs faster end to end, with more reasoning detail for debugging. New configurations: schema descriptions and topics Two new configurations give the agent more of the context it needs. Both are available for SQL data sources on the preview runtime. Schema descriptions Schema descriptions attach business context to the objects in your data source — what a table holds, what a column represents, and how each should be used. The agent uses them to interpret large or ambiguous schemas and generate a more accurate query. They matter most where object names don’t carry meaning on their own, or where several tables and columns look interchangeable. Figure: Schema descriptions add business context to schema objects, helping the data agent interpret your data. Topics Topics help you give the agent detailed guidance without sending every instruction with every question. The agent searches your topic content and uses only the sections relevant to the current request. Use data source instructions for rules that must always apply, and use topics for deeper guidance that applies only to a specific subject. You can write topics in Markdown, import multiple files, and review the run steps to see which topics the agent selected. Figure: Topics organize subject-specific instructions in Markdown to guide the data agent’s query generation. Internal benchmark testing: Advanced NL2SQL on Preview Runtime vs NL2SQL on Standard Runtime Better reasoning matters only if it produces better answers without making users wait substantially longer. In an internal benchmark set evaluating answer quality and end-to-end latency, the preview runtime delivered the stronger outcome, answering more questions correctly than the standard runtime while reducing both median and tail latency. Smart-answer accuracy increased from 78.8% to 90.9%. Latency improved at the same time: P50: 28.4 seconds to 26.0 seconds P95: 46.8 seconds to 32.9 seconds Figure: Smart answer accuracy and end-to-end latency, standard runtime versus the Advanced NL2SQL preview, on an internal benchmark set. Accuracy rises 12.1 points to 90.9%, while P50 latency falls 8.3% and P95 falls 29.8%. *Performance results are based on Microsoft internal testing conducted in 2026 using an internal evaluation set comparing standard NL2SQL to the advanced NL2SQL preview in Microsoft Fabric. Actual results may vary depending on workload, data model, and configuration. The P95 result shows the largest latency improvement in this benchmark, with latency decreasing from 46.8 seconds to 32.9 seconds. This improvement is most relevant to the slower, more complex queries represented at the tail of the distribution. Advanced DAX generation: agentic reasoning over semantic models The agent calls tools, inspects results, and refines its approach to answer harder questions. Instance value indexing searches real column values, so filters match your data. And you get consistent results across Data Agent, Power BI, and M365 Copilot. For users, this means the agent is more likely to resolve filter terms to the values stored in the semantic model and return consistent answers across supported Microsoft experiences. Internal benchmark testing: Advanced DAX generation improves accuracy while cutting latency Our internal Advanced NL2DAX evaluation showed improvements in both answer accuracy and latency. Smart-answer accuracy increased from 89.15% to 91.86%. At the same time: P50 latency: 39.1 seconds to 20.4 seconds P95 latency: 97.5 seconds to 34.7 seconds This represents an approximately 48% reduction in P50 latency and a 64% reduction in P95 latency, along with a 2.7 percentage-point increase in smart-answer accuracy. *Performance results are based on Microsoft internal testing conducted in 2026 using an internal DAX generation evaluation set comparing the baseline to the advanced DAX generation preview in Microsoft Fabric. Actual results may vary depending on workload, semantic model, and configuration. Rolling out the runtime improvements We'll begin rolling the core Advanced NL2SQL and advanced DAX generation improvements out to the standard runtime on November 2, with the rollout expected to complete by November 10. As part of this rollout, the standard runtime will also move to GPT-5.6 Terra. As the rollout reaches your environment, data agents using the standard runtime will receive these improvements and the model upgrade by default, with no opt-in required. What is moving to the standard runtime: Advanced NL2SQL: improved example-query following, filter-value substitution, and data exploration. Advanced DAX generation: the agentic tool-calling loop and instance value indexing. Model upgrade: The standard runtime will move to GPT-5.6 Terra for orchestration and supported data agent capabilities. What stays in preview: Schema descriptions and topics for SQL sources. We believe these changes deliver significantly better results, based on improvements observed in our internal evaluations. However, results can vary by data source, model, instructions, examples, and workload. Creators can re-test and evaluate their agents on the standard runtime between November 2 and November 10 as the rollout reaches their environment, or try the changes now on the preview runtime. Moving from one-shot generation to an agent that explores data can change how existing instructions and examples are interpreted. We recommend re-testing: Run your existing question set against the standard runtime as the rollout reaches your environment between November 2 and November 10, or switch to the preview runtime to try the changes now. Rerun your evaluations with the Fabric data agent SDK and a ground-truth set. Compare the new results with your previous baseline, and use the visible reasoning steps to identify where the agent’s assumptions differ. Tune your example queries and instructions based on what the evaluation shows. The agent now explores your data and adapts examples through substitution, so guidance written to work around the previous behavior may no longer be needed. Re-testing and evaluating your agents will help you confirm the expected quality improvements for your own data and scenarios. Next steps If you build data agents in Microsoft Fabric, re-test your agents on the standard runtime as the rollout reaches your environment between November 2 and November 10, or try the changes now on the preview runtime. For more details: Fabric data agent runtime: Learn what the standard and preview runtimes each contain, how to switch between them, and what’s in the preview runtime today. SQL sources in Fabric data agent: Learn how NL2SQL and Advanced NL2SQL generate queries over Lakehouse, Data Warehouse, SQL Database and Mirrored Database sources, and which configurations each one can use. Semantic model best practices for data agent: Semantic model best practices for data agent: Learn about advanced DAX generation and how to prepare a semantic model so the agent can answer questions accurately.Get ready for table discovery in OneLake Catalog search (Preview)
In late September, OneLake Catalog will begin surfacing tables as searchable objects in Microsoft Fabric. This change expands which table metadata users can discover based on their existing item permissions, so administrators may want to review the related tenant setting before rollout. To give organizations time to prepare, the controlling tenant setting is already available in the Fabric admin portal. Administrators can review and configure this setting before table search becomes available. Tables are the first object, an entity contained inside a Microsoft Fabric item, that OneLake Catalog will return as a standalone search result. Bringing objects into search makes the catalog more useful for everyday discovery because they are often where analysis, reporting, and development work begins. Find tables directly Finding the right data often starts below the item level. You may know a table name, its purpose, or only the name of a column it contains, but not which item contains it. Table discovery will let you search directly for tables in semantic models, lakehouses, and mirrored databases without first locating and opening the parent item. We plan to support tables from more Microsoft Fabric item types, along with additional object types, as OneLake Catalog expands beyond item-level discovery. Each matching table will appear as its own result rather than as metadata attached to the parent item. Columns will not appear as standalone results, but you can still find the relevant table when a column name is your only starting point. Table discovery will be available in Fabric’s global search UI and programmatically through the OneLake Catalog Search API. You will be able to search by table name or description or use an exact column-name match to find its containing table, without knowing the workspace or parent item in advance. You can also access search through the Fabric Core remote MCP server, Fabric local MCP server, or the search skill from the Fabric Skills library. These options bring the same permission-aware discovery to applications, AI agents, GitHub Copilot, and other compatible AI coding tools. Understand how permissions affect table visibility Before table search becomes available, administrators should understand how existing permissions affect what users can discover. This change does not grant access to additional data, but it may allow users to discover metadata for tables contained within items they can already access. You can discover a table only if you have Read control-plane permission or higher on its parent item. You do not need Read All or Read Data permission to find a table in search. Data-plane permissions, including OneLake security, do not change whether a table appears in catalog search. The relevant workload still enforces its data-access permissions when you open, query, or otherwise use the table. Catalog discoverability and data access remain separate decisions. Tables in semantic models protected by object-level security are excluded from search. Object-level security can protect table and column names as well as their metadata, so OneLake Catalog currently does not surface tables from those models. Control object discovery with a tenant setting Before table search rolls out, Fabric administrators should review the Users can find objects in search tenant setting. This setting controls whether users can discover contained objects such as tables through Global Search and the Search API. The setting is enabled by default, allowing users to find supported objects when they have access to the parent item. When it is disabled, search results are limited to top-level Fabric items, such as lakehouses and reports. Disabling the setting does not change access to the underlying items or data; it changes whether contained objects appear in search. Additional search improvements Apart from table discovery, this release also introduces several improvements in search for Microsoft Fabric items. Get richer item results New filters and expanded metadata make item results more precise and useful. You can: Return and filter item results by endorsement status, workspace ID, and sensitivity label ID. Find dataflows and dashboards in search results. Identify reports and dashboards included in a workspace app and retrieve the relevant app context. This context helps you distinguish between similar items, apply governance signals, and choose the right result without making additional requests. These improvements provide more ways to narrow large result sets and enough context to understand where an item belongs before you open it. Refine searches with new operators New query operators give you more control over how search interprets your terms: Use quotation marks (" ") to search for an exact phrase. Use an asterisk (*) as a wildcard for multiple characters. Use a question mark (?) as a wildcard for a single character. Use double ampersands (&&) to return only results that contain all specified terms. These operators help you narrow results when names are similar or when you know only part of a name. Prepare for rollout Tables are the starting point for discovery below the Fabric item level. The same catalog model can support more object types over time while preserving parent context, permission trimming, and a consistent way to search. For now, table search addresses a common need: finding the table you want from its name, description, or an exact column-name match without already knowing where it lives. Table search will begin rolling out late September. Before rollout, administrators should review the tenant setting, and developers can prepare the search experiences they plan to offer: Review the Users can find objects in search tenant setting in the Fabric admin portal and decide whether object discovery should be enabled for your organization. Explore the OneLake Catalog Search API if you build data portals, governance tools, or other discovery experiences. Connect an AI development tool through a Fabric MCP server or install the search skill from the Fabric Skills library to prepare agentic discovery workflows.Fabric March 2026 Feature Summary
Welcome to the Fabric March 2026 Feature Summary—and welcome to FabCon! As we kick off FabCon, this update captures the momentum we’re seeing across the Fabric platform and the conversations happening with customers and partners right now. March brings a wide range of enhancements across governance, data engineering, real-time intelligence, data science, extensibility, and AI—all designed to help teams build, operate, and scale end‑to‑end data solutions with confidence. Many of the capabilities highlighted here reflect direct feedback from the community and real‑world usage we’ve learned from—including insights shared leading up to (and during) FabCon. We are eager to share what’s new and to continue the conversation throughout the week. If you haven’t already, check out Arun Ulag’s hero blog “FabCon and SQLCon 2026: Unifying databases and Fabric on a single, complete platform” for a complete look at all of our FabCon and SQLCon announcements across both Fabric and our database offerings. Contents https://youtu.be/xhrSMNNX5ho?si=aTWiAMq0PvtTxej8 Events and Announcements Don’t miss the next Monthly Data Days Sessions On March 26 we have a special edition of Fabric Data Days featuring two topics. Join us at 8 AM Pacific for a session on getting started with Fabric IQ. Then at 3 PM Pacific we’ll discuss mapping and spatial analytics in Fabric. Register now! Couldn't make it to Atlanta or just want more FabCon + SQLCon? Join us in Barcelona this September. FabCon Europe is happening again in 2026. Mark your calendars for September 28 – October 1, 2026. Register now to access Super Early Bird pricing! Fabric Platform OneLake Catalog Govern for admins (Generally Available) In today’s data-driven world, effective data governance is crucial to ensure the integrity, security, and usability of data. OneLake catalog is available for Fabric admins, providing tools and insights to govern and secure data estates within Fabric in one place. Figure: OneLake Catalog: Govern for Admin view Figure: OneLake Catalog: Govern for admin—view more report. OneLake Catalog search API and MCP tool (Preview) OneLake Catalog’s Search API brings cross-workspace discovery to code. Instead of traversing workspace-by-workspace and “listing everything,” a single search request can locate matching items across your accessible estate based on catalog metadata and the user’s permissions. Search is designed to help even when the exact name isn’t known. Free-text matching includes the item’s display name and description, so a keyword you remember is often enough to find the right entry. Results can be filtered by the item's type to narrow down the scope of your search. The set of supported metadata signals and filters is expected to grow, enabling richer and more targeted discovery scenarios. The catalog search capability is also included as a built-in tool in the Fabric Core MCP server so AI agents can reliably locate the right Fabric asset as part of a broader workflow, then continue with follow‑up actions using other tools. Workspace tags (Generally Available) Fabric tags add meaningful metadata so people can find the right content faster and organize it consistently. That capability is available for workspaces. Workspace tags add shared context (like team, project, or cost center) at the workspace level, helping teams discover and manage workspaces more efficiently, while also enabling scalable governance through APIs. Figure: Workspace settings screen showing applied workspace tags Workspace tags are built on the existing Fabric tags model: tags are defined once, then applied on items and workspaces. Workspace admins can apply and remove tags in workspace settings, making it easy to add shared context at the workspace level. A workspace can have up to 10 tags applied. Workspace tags are surfaced in key discovery experiences so they’re usable in day-to-day navigation: Workspaces can be filtered by tags both in the workspaces list and in OneLake Catalog Explorer, a tags indicator also appears in the workspaces list and in OneLake Catalog Explorer next to every tagged workspace. Tag names are shown on the workspace screen itself, making the workspace context immediately visible. Tagging can be retrieved and managed at scale using APIs, enabling consistent application and reporting across workspaces. Data loss prevention policies for Fabric—Extending restrict access to structured data in OneLake (Preview) When handling sensitive data, it might be challenging to find the right balance between federating data and keeping it secure and compliant. Data Loss Prevention (DLP) policies enable organizations to detect sensitive data and surface it to users and admins when it is found. The Restrict Access action allows you to restrict access to your data once the sensitive information is detected. DLP Restrict Access reduces the risk of exposure to unauthorized users, without slowing analytics or collaboration. Customers can scale Fabric with confidence, meeting compliance requirements while enabling secure, enterprise-wide data sharing. With this release, you’ll be able to apply access restrictions through DLP on: Warehouses KQL databases SQL databases Lakehouses (previously supported) Semantic models (previously supported) Cosmos DB and mirrored databases are coming soon. Figure: Fabric warehouse with a restrict access indication Admins can ensure that sensitive data is protected consistently wherever it lives and however it is accessed. Learn more about restrict access in DLP. Lakehouse Signals in IRM (Generally Available) Microsoft Purview Insider Risk Management cross-references millions of signals across all your products, to create comprehensive profiles of potentially unethical behavior inside your organization. Using Lakehouse indicators in Insider Risk Management enables security teams to detect and investigate risky data activity in OneLake with greater precision and context. By incorporating Fabric Lakehouse signals directly into insider risk policies, security teams can correlate data access and movement with DLP, labeling, and audit signals in a single investigation experience—reducing blind spots and accelerating response to potential data exfiltration or misuse. This provides stronger protection for high‑value analytics data while maintaining built‑in privacy controls and avoiding the operational overhead of deploying separate monitoring tools. Figure: Lakehouse indicators used within the IRM tool Learn more about Fabric indicators in Insider Risk Management. Quick policy for data theft for Fabric (Generally Available) A new quick policy for the Data Theft rule is available for Fabric. This streamlined experience makes it easier to set up protection against data exfiltration scenarios, helping security teams take action faster when sensitive Fabric data is at risk. Learn more about IRM quick policies. Insider Risk Management PAYG Usage Report (Generally Available) The Microsoft Purview Insider Risk Management pay-as-you-go feature usage report is designed to provide transparency to customers, enabling more accurate budget planning and policy tuning. IRM admins can check the distribution of PAYG processing units billed across workloads (Fabric), sub-workloads (Power BI, Lakehouse), and indicators (downloading Power BI reports, etc.) to fine-tune their policies and plan PAYG budgets accordingly. Figure: Pay-as-you-go Usage Report Purview DSPM for AI for Fabric Copilots and data agents (Preview) As AI adoption accelerates, organizations need built‑in protections to keep data safe. With Purview Data Security Posture Management (DSPM) for AI, customers gain visibility and control over AI interactions. DSPM for AI helps teams spot sensitive data risks in AI prompts and responses, identify risky AI behavior, and apply consistent governance using familiar tools like DSPM, Insider Risk Management, Audit, and eDiscovery—so organizations can move faster with AI, without compromising security or compliance. bric_Copilot Figure: Purview DSPM for AI report showing Data Agent interaction in Fabric Learn more about DSPM for Fabric Copilots. Branched workspace with Git integration (Preview) Branched workspace is a new developer experience designed to simplify how teams work with feature workspaces during a branch‑out flow. With clearer visual cues and richer context, developers can easily understand workspace relationships and work more confidently when branching and iterating on features. This feature will be released by the end of March 2026. Figure: Fabric workspace tree showing the new relation between workspace and branched workspace Follow our new Git developer experiences in Microsoft Fabric (Preview) announcement. Selective branching with Git integration (Preview) Fabric Git Integration Branch-out with selective branching introduces a more focused branch‑out experience in Fabric. Developers can select only the items they need for a feature, reducing clutter in the target workspace, improving reliability, and accelerating time‑to‑code. By working with a smaller, purpose‑built workspace, developers can iterate faster and with greater confidence. Figure: Branch-out selective branching dialog Follow our new Git developer experiences in Microsoft Fabric (Preview) blog announcement. Compare code changes with Git integration (Preview) The new compare code changes experience helps developers confidently sync their Fabric workspace with a connected Git branch by clearly showing what changed before taking action. It provides a familiar code‑compare experience that highlights the exact differences since the last sync—down to the item and file level—whether the change originated in the workspace or in the repository. This makes it easier to review updates, understand their impact, and resolve conflicts by comparing workspace and Git versions side by side before committing, updating, or undoing changes. Figure: Git Integration code compares changes dialog Follow our new Git developer experiences in Microsoft Fabric (Preview) blog announcement. Connection reference item type in Variable Library (Preview) The new connection reference item type in Variable Library introduces a new way to manage external data connections in Microsoft Fabric. This new variable type lets you reference existing connections—such as Azure SQL or Snowflake—by storing a connection ID in the Variable Library, instead of embedding static connection strings in code. Figure: Variable Library “connection reference” item type option Connection reference variables work seamlessly with CI/CD and Git, enable safer environment‑specific configuration across dev, test, and prod, and ensure only authorized connections can be selected through the UI. This makes it easier to build, deploy, and manage Fabric solutions with cleaner configuration, stronger governance, and improved portability across CI/CD stages. Bulk import and export items definition APIs (Preview) These APIs enable you to programmatically export, import, and synchronize Fabric item definitions across workspaces at scale—all through the Fabric REST API. Every Fabric item—whether it’s a Notebook, Report, Semantic Model, Data Pipeline, or KQL Dashboard—has an underlying item definition: a portable schema containing the item’s full configuration and content (encoded in Base64). The Import & Export Batch APIs let you: Export item definitions individually or in bulk from any workspace Import (create) items from definitions into a target workspace Update existing item definitions in-place for continuous deployment List & paginate through all items in a workspace for batch operations Key scenarios Workspace migration: Moving items across workspaces, tenants, or regions is one of the most common requests from Fabric customers. The batch APIs let you export all items from a source workspace into a portable JSON manifest, then import them into any target workspace. This is invaluable for replicating environments across different tenants and cloning a production workspace for testing purposes. CI/CD and DevOps integration: To support enterprise DevOps practices in Microsoft Fabric, organizations can integrate the new Bulk Export and Import APIs into their CI/CD pipelines. Fabric item definitions can be treated as code—exported and versioned in Git using Fabric Git Integration or the bulk-export API, validated through pull request workflows, and promoted through a well-defined release process. When deploying across workspaces, the bulk-import API enables consistent, automated promotion into test and production environments using the underlying Fabric dependency logic that creates new items in the correct order, retains the original relations, and updates existing ones in place. Metadata backup and recovery: Schedule periodic batch exports to capture the full state of your workspace as versioned JSON manifests. Store them in Azure Blob Storage, a Git repository, or any durable storage. If something goes wrong, re-import the manifest to restore your workspace to a known-good state. Metadata scanning and lineage analysis: Tools that analyze report definitions to discover data lineage—such as which semantic model columns are used in each report—can extract hundreds of report definitions in bulk instead of one at a time, reducing scan time.Export (read) operationsMethod Endpoint Description POST /workspaces/{workspaceId}/items/bulkExportDefinitions?beta=true Export an item’s full definition as Base64-encoded parts. May return 202 for LRO. Import (write) operationsMethod Endpoint Description POST /workspaces/{workspaceId}/items/bulkImportDefinitions?beta=true Create/Update an existing item’s definition in-place. Ideal for CI/CD sync. Resources Full announcement: Bulk import/export items definition APIs API documentation: Fabric Items API Reference Comprehensive guide: Item Management Overview Item definition structure and formats: Item Definition Reference How to handle async operations with polling: Long-Running Operations Guide App registration and authentication setup: Microsoft Entra ID Documentation CI/CD tutorial using Bulk API: CI/CD tutorial using the Bulk Export and Import APIs Fabric CLI v1.5—Power BI Scenarios, CI/CD Deployments, and DX Improvements The Fabric CLI v1.5 is the most scenario-driven update yet. Power BI developers can now trigger semantic model refreshes, rebind reports, and script end-to-end deployment workflows—all from the terminal, without portal context-switching. The release also adds a new deploy command for CI/CD, interactive REPL mode, JMESPath filtering, notebook export in multiple formats, Python 3.13 support, and expanded coverage for Fabric items. Many of these improvements are community-contributed, making the CLI a comprehensive open-source automation surface for Fabric. CI/CD deployments from the CLI—deploy workspaces in One Command A new deploy command integrates the Fabric CI/CD Python library directly into the Fabric CLI, enabling full workspace deployments—including item rebinding and configuration—from a single command. Teams can run deployments from their terminal, GitHub Actions, or Azure DevOps pipelines. Combined with Service Principal authentication and federated credentials for GitHub OIDC, this enables zero-touch, Git-based promotion workflows that fit modern DevOps practices—no custom scripts or additional tools required. For usage examples, refer to the CI/CD examples and setup guide. Fabric CLI as an Execution Layer for AI Agents Fabric CLI is designed to work well with AI agents. A structured agent instructions file and a dedicated Fabric CLI Skill provide AI assistants like GitHub Copilot and Claude with the context they need to generate correct CLI commands from natural language. Improved error messages with actionable guidance help agents self-correct, and the interactive REPL mode enables persistent terminal sessions for multi-step agent workflows. Using a CLI as the execution layer for AI agents is an emerging industry pattern—instead of agents calling raw REST APIs (which require extensive token-heavy context about endpoints, auth, and payloads), agents issue concise CLI commands that encapsulate that complexity, making AI-driven Fabric automation more practical and reliable. Learn more with Fabric CLI agent docs and AI assets on GitHub. Fabric Remote MCP Server: AI agents operate directly in your Fabric environment Fabric Remote MCP is a cloud-hosted MCP server that allows AI agents to perform real operations in your Fabric environment—create workspaces, manage permissions, work with item definitions, and more. No local installation is required. Agents authenticate via Entra ID and operate within your existing RBAC boundaries, with every tool invocation recorded in audit logs. The preview launches with capabilities spanning workspace management, item CRUD and definitions, and permission management. It works with any MCP-compatible client, including GitHub Copilot, Cursor, and Claude Desktop. Learn more in this blog post: Introducing Fabric MCP (Preview). Fabric MCP AI code assistants (Generally Available) The Fabric Local MCP is an open-source MCP server that runs on your machine. This solution integrates AI coding assistants with the comprehensive Fabric API, offering OpenAPI specifications, best-practice guidelines, item definition schemas, and example payloads to enable agents to produce precise, production-ready code while minimizing errors. OneLake tools enable live file operations including upload, download, table inspection, and item creation. This update introduces integrated authentication, automatic retry, production SLAs, and telemetry. Install via npx Microsoft/fabric-mcp in any MCP-compatible client—it works with VS Code, Claude Desktop, Cursor, and more. Fabric Local MCP on GitHub.Fabric Fabric Extensibility Extensibility (Generally Available) After six months in preview mode, gathering feedback, resolving bugs, and strengthening the platform, we’ve reached the next milestone. Partners and customers can build, validate, and publish custom Fabric workloads to production with full Microsoft support. Key highlights: All core capabilities are stable and supported: OneLake storage, native item lifecycle, Entra token acquisition, iFrame relaxation, Workload Hub publishing. The Starter Kit ships with production-ready UI components (ItemEditor, WizardControl, OneLakeView, and more) that reduce time to first workload. GitHub Copilot integration and a new DevContainer/GitHub Codespaces setup reduce setup effort—no local machine required. The first Fabric Extensibility Community Contest drew strong community participation, with real workloads already appearing in the Workload Hub. Learn more about Fabric Extensibility (Generally Available). CI/CD & remote support (Preview) Three new features further enhance the professional development experience for Fabric workloads. CI/CD Support Workload items are first-class citizens in Fabric's CI/CD platform. Items participate in Git integration and Deployment Pipelines with no custom tooling. Variable Library support means items automatically pick up workspace-specific configuration (e.g., the right Lakehouse reference) when promoted across dev, test, and production—no hard-coded IDs, no manual reconfiguration. Figure: CICD enablement for Hello World Sample Variable Library Support Items can be read from Fabric's Variable Library, allowing workspace-specific configuration (e.g., the right Lakehouse reference) to resolve automatically when an item is promoted across dev, test, and production stages—no hard-coded IDs, no manual reconfiguration, and no custom deployment hook logic required. An opt-in webhook that fires whenever a workload item is created, updated, or deleted—regardless of whether it happened through the UI, the REST API, or a CI/CD pipeline. It’s designed for licensing checks, infrastructure provisioning, and external system synchronization. There’s no impact on workloads that don't register an endpoint. Figure: Variable Picker within Fabric Cloud Shell Item Remote lifecycle notification API Workloads are no longer just passive objects sitting in a workspace. The Remote Lifecycle Notification API is an opt-in capability—there is no requirement to use it. If your workload does not need backend notifications, you simply don't register an endpoint, and everything works exactly as before. Fabric Scheduler / Remote Jobs This feature allows workload items to expose named job types that users can schedule directly from Fabric. When a scheduled job fires, Fabric calls a registered endpoint on your workload backend—passing along the item context and a delegated user token. For all these features, you’ll find samples in the Toolkit Starter Kit. Learn more about Fabric Extensibility CI/CD and remote capabilities in this blog post. What's new in workload management As the Fabric extensibility ecosystem grows, with partners publishing workloads and organizations building custom solutions, managing workloads at scale demands more than a single settings page. IT admins need centralized governance and a clear overview of what's being used across the organization, and workspace teams need self-service agility. Three key workload management features for Microsoft Fabric Extensibility will launch by April 1, 2026: Workload admin portal (Generally available) Add workload to workspace (Generally available) Workload management admin APIs (Preview) These will enhance governance through portal, API, and self-service capabilities. Admin portal: centralized admin workload overview (Generally Available) The Fabric Admin Portal now includes a dedicated Manage Workloads tab, a single pane of glass for workload governance across your organization. Centralized workload visibility: view all workloads available for assignment in your tenant in a single centralized view, including status information and workload details. Tenant assignment controls: manage workload assignment at the tenant and workspace level. Add workload to workspace (Generally available) The workspace-level workload assignment was previously introduced in Preview. It allows workspace admins to add workloads directly to one or more workspaces. How it works (Workspace admins): Navigate to the Workload Hub from the left menu in Microsoft Fabric or from your workspace settings. Browse or search for the workload you want to add. Select "Add Workload" and select "To Workspace" from the dropdown. Select your workspaces: search, check the workspaces you want, and use "View more/less" to manage the list. Select "Add": the workload is immediately available in your selected workspaces. Workload Management Admin APIs: Overview and Control at Scale (Preview) Capabilities For Fabric admins who need a programmatic view of their workload landscape, the new Workload Management Admin APIs provide governance and oversight across the tenant through a REST interface. List all workloads: view all workloads available to be added in the tenant, and view which workloads were added. List all workload assignments in the tenant. Drill down into a specific workload and view where it was added (tenant, workspace, capacity). Manage workload assignments (add or remove) to capacities, workspaces, and tenant. Self-service workload publishing (Generally Available) A frequent question from ISV partners using the Microsoft Fabric Extensibility Toolkit is: "How do I get started publishing?" Key features Self-service workload publishing is expected to be generally available by the end of March 2026. ISV partners will be able to publish workloads directly to selected customer tenants for private preview without requiring a manual submission request. This can accelerate time to market and support faster iteration with customers. Self-Service Workload Publishing gives ISV partners full control over their private preview journey: Publish to up to 20 customer tenants: share your workload with selected customers for testing and validation, no Microsoft certification required. Workload name reservation: reserve your globally unique workload name (e.g., Contoso.DataQuality) to protect your brand identity before formal publication. Automated validation: your workload package is automatically validated against manifest schema, naming conventions, and security requirements at upload time. Seamless path to general availability: once validated with customers, use the same workload package to pursue formal certification and publish to the global Workload Hub. OneLake Third-party support for OneLake security This month, we announced third‑party support for OneLake security, taking an important step toward interoperable data security. As customers increasingly build lake‑first architectures on open formats like Delta and Iceberg, they expect the freedom to use multiple analytics engines without copying data or redefining security. OneLake security addresses this need by enabling security to be defined once and enforced consistently wherever data is accessed. At the core of this capability is the authorized engine model. Security policies—including role‑based permissions, row‑level security (RLS), and column‑level security (CLS)—are centrally defined and managed in OneLake, while enforcement happens at query time inside the engine reading the data. Authorized third‑party engines securely retrieve the relevant metadata and effective security definitions through OneLake APIs and apply them during query execution. This ensures users see only the rows and columns they are permitted to access, while OneLake remains the single source of truth for access control. To support adoption, we’ve published implementation guidance and setup documentation for both engine builders and users. The APIs are designed to be engine-agnostic and easy to integrate by providing pre-computed effective access definitions. Looking ahead, we’ll continue evolving OneLake security APIs, including adding support for bitmap-based RLS enforcement. With this release, data vendors can integrate directly with OneLake security, customers can maintain a single security model, and users gain the flexibility to query OneLake data using the engines of their choice. OneLake file explorer (Generally Available) You can easily access and organize all your OneLake data from Windows using the OneLake file explorer. The file explorer lets you browse every workspace and data asset, and upload, download, or edit these files using the same familiar experience as OneDrive. By bringing data lakes into the Windows file system, the file explorer makes enterprise data more accessible for business users. Data Engineering Fabric Runtime 2.0 (Preview) Fabric Runtime 2.0 (Preview) is a next-generation runtime that is purpose-built for large-scale data computations in Microsoft Fabric and introduces key features and components that enable scalable analytics and advanced workloads. Apache Spark: 4.0 Components include Operating System: Azure Linux 3.0 (Mariner 3.0) Java: 21 Scala: 2.13 Python: 3.12 Delta Lake: 4.0 This screenshot demonstrates how you can switch to Runtime 2.0 at the Workspace settings and the Environment levels. Figure: Change runtime at the workspace settings level Explore the full documentation and start using Runtime 2.0 in Fabric. Custom Live Pools for Fabric Data Engineering Modern data engineering workloads are rarely one‑size‑fits‑all. Teams often need predictable performance, isolated resources, or customized configurations for critical production pipelines and high‑value interactive development. At the same time, Spark session startup times can degrade in real-world enterprise environments, especially when users have custom library dependencies. Workspaces or tenants are secured with Private Links or Managed Private Endpoints. In these scenarios, Spark clusters must be created on demand within strict network boundaries, and libraries need to be resolved and installed dynamically, adding noticeable startup latency. Custom Live Pools address this challenge by introducing dedicated, long‑lived Spark pools that stay warm inside your network boundary and come preconfigured with the required dependencies. With Custom Live Pools, Fabric Data Engineering now enables you to: Create dedicated Spark pools and schedule them tailored to your workload needs. Reduce session startup overhead by keeping pools warm with libraries preinstalled. Run reliably within Managed VNets and Private Link–enabled environments. ompute_configuration_panel_within_a_data_analytics_platfo Figure: Animated GIF demonstrating the setup of custom live pools in an Environment Because these pools are already provisioned within the workspace’s network boundary and fully initialized with dependencies, users can start working immediately, without paying the repeated cost of cluster spin‑up and library installation. Custom Live Pools are ideal for: Production pipelines that require consistent SLAs. High‑value interactive notebooks used by data developers. Teams operating in secure or regulated environments. How to set up a Custom Live Pool: Navigate to your Compute tab in your Environment. Select Spark pool and enable the option for Live Pool. Specify the Schedule, Time period of inactivity, and Retrigger frequency. Job concurrency and queue monitoring experience for Fabric Data Engineering As organizations scale their Fabric usage, understanding what’s running, what’s queued, and why becomes essential. The new job concurrency and queue monitoring experience delivers deep visibility into Spark workload execution across your environment. View active, queued, and completed jobs in a single place. Understand why jobs are queued and how concurrency limits are applied. Identify bottlenecks caused by capacity or concurrency constraints. Make informed decisions to tune workload scheduling and resource allocation. Figure: GIF demonstrating the new job concurrency and queue monitoring view in the Data Engineering/Science Spark settings page of Workspace settings Accessing workspace monitoring To view concurrency and queue signals for your specific workspace: Navigate to Workspace settings. Select Data Engineering/Science > Spark settings. Select Jobs to view the live view of your workspace level Spark queue and concurrency. Resource Profiles for Fabric Data Engineering Modern data engineering teams shouldn’t need to be Spark experts to get great performance. With Resource Profiles in Fabric Data Engineering, users simply describe what they’re trying to do, and Fabric automatically recommends the optimal compute configuration. Figure: GIF demonstrating the new resource profiles experience in workspace settings Simple inputs, smart recommendations Instead of tuning dozens of Spark settings, users provide a few high‑level workload details through an intuitive UI: Primary use case, such as a specific medallion layer (Bronze, Silver, or Gold) or task‑based optimization (read‑heavy or write‑heavy workloads). Typical data volume. Data characteristics, such as whether input data contains many small files. Maximum capacity units (CU) for the Spark pool. Once these inputs are provided, users select Get recommendation, and Fabric automatically generates an optimized configuration tailored to that workload. Based on the inputs shown above, Fabric recommends: The appropriate Resource profile. Node family and size. Autoscale and dynamic executor settings. Optimized Spark driver and executor cores and memory. A compatible runtime version. All recommendations are derived from proven best practices and internal performance tuning, removing guesswork and trial‑and‑error. Where to configure Users can enable and manage Resource Profiles from workspace settings: Go to Workspace settings > Data Engineering and Data Science > Resource optimization. Select or edit the optimized profile for the workspace. Rerun the Optimize for your use case flow as workloads evolve. Apply consistent configurations across all Spark workloads in the workspace. Once configured, all notebooks and pipeline‑triggered Spark jobs inherit these optimized settings automatically, without requiring per‑notebook configuration. Why this matters This experience enables: Performance by default: optimized compute without manual tuning. Consistency: the same performance characteristics across users and jobs. Better price‑performance: right‑sized resources aligned to workload intent. Lower operational overhead: fewer tuning cycles and support escalations. As workloads change over time, teams can simply revisit the optimization flow, update a few inputs, and let Fabric adapt the configuration—without rewriting code or Spark settings. Figure: Introduction to Resource Profiles Experience Figure: Recommendations generated based on user inputs To learn more about the Resource Profiles experience in Microsoft Fabric Data Engineering, refer to the Microsoft Learn documentation. Installing libraries with Quick mode in Spark Environment (Preview) Managing libraries shouldn’t slow down your development workflow. In Microsoft Fabric Environments, we’re introducing a more efficient way to iterate on libraries while keeping production workloads stable and reliable. Fabric Environments now support two complementary library installation modes that you can use side by side: Quick mode: a fast, on-demand installation path designed for development and experimentation, where libraries are installed when a notebook runs. This avoids heavy processing during the environment publishing and significantly reduces publish time and notebook startup latency when you’re iterating on lightweight or frequently changing dependencies. Full mode: a snapshot‑based installation path optimized for production workloads and pipelines, where libraries are fully resolved, validated against the Spark runtime, and published as a stable snapshot to ensure consistency and reproducibility. Figure: Add libraries in Quick mode and Full mode This new feature lets you move faster during development without compromising production stability. You can keep your core, production‑ready libraries in the snapshot‑based mode, while using the on‑demand path to quickly test new packages or iterate on custom libraries, all within the same Environment. Dynamic session sharing limit up to 50 for high concurrency Fabric High Concurrency Spark sessions enable both interactive exploration and large‑scale, pipeline‑driven notebook execution, supporting parallel, scheduled, and event‑driven workloads at enterprise scale. Customers often achieve higher density by packing notebooks into a shared High Concurrency (HC) session using session tags, effectively fitting up to five notebooks per session to control startup overhead and cost. While effective, this approach relies on static limits and manual tuning. With this update, Fabric Data Engineering allows the maximum number of notebooks attached to a High Concurrency session to be increased up to 50, enabling dynamic session sharing at much higher scale. Where to set the configuration You can set the configuration in the Environment item that your notebooks or pipeline‑triggered notebooks use: Go to Workspace → Environments Select the Environment attached to your notebook or pipeline Open Spark Properties Add the High Concurrency configuration Set spark.highConcurrency.max to a value between 2 - 50 Note: This update does not change the default limit of five. This enables: Interactive notebooks, used for exploratory analysis and collaboration. Notebook jobs triggered by pipelines, running in parallel within shared HC sessions. Dynamic adjustment of session sharing limits based on workload intensity, cost, and price‑performance goals. By increasing the session sharing limit, customers can: Improve session acquisition times during peak load. Increase notebook density without fragmenting sessions. Tune concurrency to match workload demand rather than fixed defaults. Achieve better price‑performance efficiency while preserving isolation and fairness across jobs. To learn more about increasing your session sharing limit in High Concurrency mode, please check out Microsoft Learn documentation. Data export settings for notebooks With data export settings for notebooks, Microsoft Fabric empowers administrators with explicit, tenant-level control over how data leaves notebooks. This feature helps ensure that interactive analytics do not inadvertently become channels for data exfiltration. Administrators can restrict the downloading of notebooks, preventing files that may contain sensitive data, credentials, or proprietary logic from leaving the environment. Additionally, they can disable downloads of rich output content, such as table results generated from DataFrames, within the notebook experience. By managing these controls, Fabric admins can effectively prevent unintended data exfiltration from interactive notebook workflows and consistently enforce security and compliance policies across all workspaces and teams. Figure: New data export tenant setting for notebooks Figure: New data export tenant setting enabled What users experience when downloads are blocked When an administrator blocks data export: The Download option is removed from the notebook UI. Users can no longer download notebook files or rich output content generated from DataFrames in the notebook experience. Interactive exploration continues in‑place, but data cannot be extracted outside Fabric through the notebook UI. Figure: Notebook with download controls disabled due to tenant-level enforcement This ensures that notebooks remain a secure analysis surface, rather than a data export mechanism, without disrupting day‑to‑day exploration inside the platform. Why this matters Notebooks often contain more than just code: Embedded datasets Derived analytical results Business logic Confidential insights By controlling export behavior at the platform level, Fabric helps organizations: Reduce risk of accidental data leakage. Meet regulatory and audit requirements. Standardize governance across teams and regions. Data Export Settings for Notebooks reinforce Fabric’s commitment to secure‑by‑default analytics, enabling powerful interactive experiences without compromising enterprise security posture. Session starts insights into Fabric Data Engineering Fast session startup is critical for interactive analytics, and Fabric’s Starter Pools are designed to deliver Spark sessions in ~5 seconds by default. However, when that target isn’t met, users have historically had little visibility into why. Session Start Insights closes that gap by making session acquisition transparent, debuggable, and actionable. Why sessions don’t always start in five seconds In practice, session startup delays are almost always driven by user‑side configurations, not platform regressions. Common causes include: Custom compute configurations that prevent reuse of pre‑warmed Starter Pools. Pre‑installed libraries or environment dependencies that require cluster customization. Managed VNets or private networking that force isolated cluster provisioning. Unexpected high regional demand triggering fallback to on‑demand clusters. What Session Start Insights delivers Previously, users could see that a session was “starting,” but not what was happening under the hood. With this feature, Fabric surfaces clear, explicit reasons for session startup behavior directly in the product experience: Whether the session was served from a Starter Pool or required an on‑demand cluster. The exact reason a fast‑path session could not be used (for example, libraries, networking, or custom configs). Where time was spent during session acquisition. Using the session detail view to diagnose delays Navigate to the notebook’s session status or monitoring pane. Open Session Details for the active or recent session. Figure: Notebook with Session Details option Review the delay reason and session source (Starter Pool vs. on‑demand) Figure: Notebook with Session Details pane showing session start details This makes it immediately clear whether the delay was: Expected due to configuration choices Related to libraries or networking Learn more about session start insights in the Microsoft Learn documentation. Z-order and liquid clustering support in the Native Execution Engine With the Native Execution Engine, Fabric Data Engineering continues to raise the bar on price‑performance leadership for large‑scale analytics. Beyond execution‑time optimizations, the engine now includes native support for Z‑Order and Liquid Clustering, allowing advanced data layout techniques to fully benefit from vectorized, C++‑based execution paths. This ensures that storage‑level optimizations and execution‑level acceleration work together, delivering compounding performance gains for real‑world analytical workloads. Why this matters Modern analytical queries frequently: Filter on multiple high‑cardinality columns. Scan large Delta tables repeatedly. Rely on selective predicates to narrow down results. Without intelligent data layout, even a highly optimized execution engine can spend unnecessary time scanning data. By combining the Native Execution Engine with Z‑Order and Liquid Clustering, Fabric ensures that: Related data is co located on disk, enabling aggressive file and row‑group skipping. Queries scan fewer files and fewer bytes. CPU‑efficient native operators are paired with I/O‑efficient data access. On a one‑billion‑row dataset, internal benchmarks comparing fallback execution versus Native Execution Engine with clustering showed: 20–32 seconds absolute runtime reduction per query. Roughly 20%–27% improvement across multiple clustered column combinations. Performance gains observed consistently across different predicate shapes and data distributions. This brings a compounding performance effect: faster scans, fewer CPU cycles, and lower cost per query, without requiring users to rewrite Spark code or change query semantics. This helps deliver strong price-performance for analytics workloads. How users enable and use this 1. Enable the Native Execution Engine Users must first ensure that the Native Execution Engine is enabled for their Spark workloads (at the workspace, environment, or session level). Once enabled, supported Delta operations automatically run through native execution paths. 2. Use Z‑Order or Liquid Clustering on Delta tables Users can apply clustering using standard Delta Lake commands: Define Liquid Clustering at table creation or apply it to existing unpartitioned tables Use OPTIMIZE … ZORDER BY for multi‑column access patterns To learn more about the Z-Order and Liquid Clustering support or Native engine, refer to the Microsoft Learn documentation. Copilot for data engineering and data science Microsoft Fabric notebooks now include a context-aware Copilot experience designed to support you across the full notebook lifecycle. By automatically understanding your workspace environment—including attached Lakehouses, notebook structure, and runtime behavior—Copilot provides assistance that stays aligned with how your notebook is built and executed. It’s easy to get started with no session startup required. Choose the Copilot icon on the toolbar to open the chat panel. Copilot can help accelerate notebook development by generating and refining code, explaining unfamiliar logic, and assisting with larger notebook workflows. For more complex tasks, Copilot can first propose a plan and then help implement it across the notebook, allowing you to move from idea to working solution more quickly. Copilot also improves the troubleshooting experience when notebook executions fail. Instead of navigating long stack traces or ambiguous error messages, you can use Copilot to analyze failures, identify likely root causes, and review suggested fixes directly within the notebook. Figure: Fix with Copilot provides error summary and suggested fixes Throughout this process, built-in guardrails ensure you remain in control. Copilot suggestions are transparent, and proposed code changes can be reviewed before being applied. Together, these capabilities help teams reduce development friction, resolve issues faster, and build more reliable data workflows. Try the new Copilot experience today. To learn more, visit the Copilot for Data Engineering and Data Science documentation. Fabric notebook custom agent inside VS Code The Fabric notebook custom agent is a Fabric-native AI development agent embedded in the Fabric Data Engineering VS Code extension. It helps data engineers build, debug, and publish Microsoft Fabric notebooks and Spark workloads. Unlike generic coding assistants, this agent operates with full awareness of the Microsoft Fabric workspace, runtime, environments, and Lakehouse resources. It ensures every action—code generation, execution, artifact management, and publishing—is context-aware, validated, and safe for enterprise environments. Prior to the introduction of the Fabric Notebook custom agent within the VS Code extension, there were notable limitations in how language models understood and interacted with the Microsoft Fabric environment. For instance, when users provided a prompt such as "read the parquet file from the current default Lakehouse and save it to a delta table," the language model was unable to interpret what was meant by "default Lakehouse." As a result, it would generate standard Spark code without leveraging the built-in spark variable available within the notebook, which is essential for initializing and managing Spark sessions in the Fabric environment. With this new agent, the following code will be generated and ready to run. # Read parquet file from default lakehouse df = spark.read.parquet("Files/green_tripdata_2022-08.parquet") # Write to delta table in dbo schema df.write.mode("overwrite").format("delta").saveAsTable("dbo.raw_green_tripdata_202208") This custom agent should be automatically activated once the Notebook is open. Figure: Fabric notebook custom agent For more detail, refer to the Author notebook inside VS Code documentation. Tenant switching inside Fabric Data Engineering VS Code extension ISV and partners often collaborate with multiple end customers, each typically operating within their own dedicated Microsoft Fabric tenant. To address this need for flexibility, the Fabric Data Engineering VS Code extension now enables tenant switching. With this enhancement, ISVs and partners can easily transition between different customer projects within the same VS Code window, eliminating the need for repeated sign-in processes. This streamlined experience simplifies managing multiple projects and improves overall productivity for professionals working across diverse customer environments. To switch to a different tenant, select the currently signed-in Fabric user in the status bar and pick the target tenant from the list. Figure: Switch Fabric tenant inside VS Code Enable new kernels inside Fabric Data Engineering VS Code extension Users can now run Fabric notebooks within VS Code using a variety of new kernels. Previously, running notebooks required users to specify the language of each cell using cell magic commands and rely on PySpark as the execution environment. With this enhancement, three additional kernels have been introduced, allowing users to select their preferred programming language directly at the kernel level. This eliminates the need for cell magic commands and streamlines the process, enabling notebooks to be executed in Python, Scala, or Spark SQL natively within VS Code. Choose Microsoft Fabric Runtime from the top-level kernel list. The available languages then appear in the second panel. Figure: Microsoft Fabric Runtime entry Figure: Supported Fabric notebook languages in VS Code For more detail, please refer to the documentation Author notebook inside VS Code. Support for multiple schedules in Fabric materialized lake views MLVs now support multiple named schedules per lakehouse. Previously, all MLVs shared a single schedule, and teams needing different refresh timings resorted to notebook-triggered refreshes. This workaround bypasses dependency management, centralized error reporting, and retry logic; failures can persist for weeks undetected. Each named schedule now targets a specific subset of views. A finance pipeline can refresh hourly while an analytics pipeline runs every six hours, with no scripting required. When a schedule fires, Fabric refreshes upstream dependencies in order, runs independent views in parallel, surfaces errors centrally, and skips overlapping runs. Figure: Schedules panel for materialized lake views, showing configured schedules and available actions For more information, refer to the Schedule a materialized lake view run documentation. PySpark support for Fabric materialized lake views (Preview) MLVs now support PySpark authoring (Preview), letting data engineers create, refresh, and replace MLVs from Fabric notebooks using the DataFrameWriter API. Previously, teams wrote definitions in Spark SQL, which made custom cleansing logic, UDFs for business rules, and procedural transformations harder to express. With PySpark authoring, MLVs gain access to the entire Python ecosystem. A gold-layer MLV can score transactions against a fraud detection model, standardize addresses using a geocoding library, or validate records against external regulatory rules. All existing MLV capabilities, including data quality constraints, table properties, and scheduled refreshes, work identically with PySpark-authored definitions. Full refresh only today; optimal refresh is coming soon. For more information, refer to the PySpark reference for materialized lake views (Preview) documentation. Move data from source to Lakehouse in a few moves using Copy job Getting data into your Lakehouse should be straightforward. For many customers, the first interaction with Microsoft Fabric begins right after creating a Lakehouse and selecting Get data. With this update, Copy job appears at the top of the Get data experience in Lakehouse, making it a more discoverable way to bring data into Fabric. Whether you’re onboarding your first dataset or scaling ingestion across multiple sources, Copy job can help you move data with minimal setup so you can focus on insights instead of configuration. Fabric notebooks now support lakehouse auto‑binding when used with Git, making notebooks far more portable across environments such as dev, test, and prod. Instead of hard‑binding a notebook to a specific lakehouse, Fabric automatically resolves the correct lakehouse as the notebook moves across Git‑connected workspaces, reducing manual rebinding and environment‑specific fixes. This feature is opt‑in and must be enabled from the notebook settings page. Once enabled, it applies to all lakehouses referenced in the notebook, including the default and any additional lakehouses. The configuration is stored in a system‑managed notebook-settings .JSON file in the Git repo, which should not be edited manually. Overall, lakehouse auto‑binding helps teams focus on versioning notebook logic while keeping data and environment configuration cleanly separated. Try it out in just a few steps: Create or open a Lakehouse. Select the Get data dropdown in the ribbon. Figure: Start ingesting data into a Lakehouse directly from the Get data dropdown using Copy job Select New Copy Job You’ll be redirected to the Copy Job experience, where you can choose the source data you want to ingest from. In just a few clicks, your data is copied into the Lakehouse and ready for exploration, analysis, and downstream analytics. Learn more: What is Copy Job in Data Factory – Microsoft Fabric Notebook supports Lakehouses auto-binding in Git Fabric notebooks now support lakehouse auto-binding when used with Git flow, making notebooks more portable across environments such as dev, test, and prod. Instead of hard-binding a notebook to a specific lakehouse in the original workspace, auto-binding lets the notebook automatically resolve the linked lakehouse as it moves across Git-connected workspaces. This reduces manual rebinding and environment-specific fixes. This feature is opt‑in and must be enabled from the notebook settings page. Once enabled, it applies to all lakehouses referenced in the notebook, including the default and any additional lakehouses. Figure: Entry of auto-binding setting in notebook The configuration is stored in a system‑managed ‘notebook-settings .json’ file in the Git repo. Overall, lakehouse auto‑binding helps teams focus on versioning notebook logic while keeping data and environment configuration cleanly managed. Notebook Resources Folder Support in Git Notebook projects often depend on more than just notebook code—such as reusable Python modules, configuration files, or small supporting assets. Fabric notebooks now support committing the built‑in Resources folder to Git, enabling true end‑to‑end source control for notebook‑based projects. These resources are versioned alongside the notebook and automatically restored during Git sync. To support real‑world workflows, this feature includes fine‑grained controls. Teams can define Git exclusion rules or use standard .gitignore files inside the Resources built in folder to avoid tracking large files, temporary assets, generated outputs, or test data. Figure: Define resources in git settings in notebook The feature is disabled by default to ensure safe adoption and does not introduce noticeable performance impact during commit or sync. The support for Environment resources folder, deployment pipelines, and public APIs is coming soon. Learn more: Notebook source control and deployment - Microsoft Fabric Fabric notebook public APIs (Generally Available) Fabric Notebook Public APIs enable notebooks to be managed and executed programmatically as first‑class assets. The APIs provide full CRUD support—enabling teams to create, update, list, and delete notebooks at scale—making them ideal for CI/CD and automated environment management. In addition, notebooks can be executed on demand via the Job Scheduler API. You can parameterize notebook runs, customize session configuration, specify environments and lakehouses, monitor execution status, and cancel runs if needed. Secure service principal authentication is also supported. A key enhancement is the ability for notebook runs to return exit values, enabling conditional branching and richer orchestration in pipelines. Together, these APIs unlock seamless integration with Fabric pipelines, external schedulers, and enterprise automation platforms. Learn more: Items - REST API (Core) and Job Scheduler - REST API (Core). Improved Copilot completion for Fabric notebooks We’re introducing upgraded Copilot completion in Fabric notebooks to deliver a faster, more accurate, and more intuitive coding experience. With this update, auto-completion is closer to what developers expect from VS Code‑style inline suggestions, helping you stay in flow while writing notebook code.You can enable the feature from the Copilot completion button in the notebook status bar. It supports both Python and PySpark notebooks. Figure: How to enable copilot completion A More Natural, Inline Coding Experience The upgraded auto‑completion is designed to work inline as you type, offering context‑aware code suggestions that better match your intent. Whether you’re writing Python logic, data transformations, or helper functions, Copilot now provides suggestions that feel more predictable, relevant, and easy to accept—reducing friction compared to earlier experiences. Faster and More Responsive Performance has been a key focus of this upgrade. Auto‑completion now responds more quickly, reducing latency between keystrokes and suggestions. This makes Copilot feel less intrusive and more like a natural extension of the editor, especially during rapid iteration or exploratory development. Higher‑Quality Suggestions That Fit Notebook Workflows Beyond speed, the quality of suggestions has improved. Copilot is better at understanding notebook context, including surrounding cells and in‑progress code, resulting in completions that require less manual editing. The goal is simple: help you write correct, readable code with fewer interruptions and less back‑and‑forth. Designed for Everyday Notebook Development This upgraded auto‑completion brings Fabric notebooks closer to the editing experience developers are already familiar with, while remaining optimized for data engineering and analytics workflows. Learn more by exploring Develop, execute, and manage notebooks - Microsoft Fabric. Create files in the notebook resources folder Fabric notebooks now let you create and manage files directly in the built‑in Resources folder, making it easier to develop and maintain notebook dependencies. You can create and edit Python modules, configuration files, and other lightweight assets alongside your notebook code and use them directly within the notebook. Figure: Entry of creating new file in notebook resources folder To learn more, refer to How to use notebooks - Microsoft Fabric. Data Science and AI Fabric data agents (Generally Available) Data sources: Build and consume data agents on a broad set of data sources, including Lakehouse, Warehouse, semantic models, Eventhouse, SQL databases, and mirrored databases. Configurations: Configure data agents using agent-level instructions, data source–specific instructions, and example queries to tailor behavior to your scenarios. Publish and share: Publishing and sharing data agents within Microsoft Fabric is generally available, making it easier to operationalize and collaborate on data agents. Figure: End-to-end data management workflow from creation through consumption This release also includes diagnostic, Git integration, and deployment pipelines as part of Microsoft Fabric’s Application Lifecycle Management (ALM) capabilities, enabling troubleshooting and lifecycle management of your agents! Advanced security and governance in data agents (Preview) Data agents in Microsoft Fabric (Preview) include capabilities that elevate security and governance standards. Through integration with Microsoft Purview, organizations gain access to comprehensive auditing, eDiscovery, data lifecycle management, communications compliance, and classification. These tools capture prompt and response telemetry along with user context, supporting enterprise protection and regulatory compliance. Additionally, we are introducing outbound access protection support for the Data Agent artifact to help mitigate sensitive data exfiltration risks and adhere to strict security policies at the individual workspace level. With these updates, organizations can monitor, control, and safeguard all data agent interactions more effectively. Data source enhancements for data agents (Preview) The latest preview brings significant enhancements to data agent source capabilities in Microsoft Fabric. Users can now connect Graph as a data source, enabling them to model and analyze complex relationships within their data for richer, AI-driven insights. Additionally, support for KQL user-defined functions (UDFs) and SQL functions is available, allowing for more sophisticated and efficient querying in KQL- and SQL-enabled sources. These enhancements make data agents more flexible and powerful, supporting faster analytics and expanded scenario coverage. Insider Risk Management PAYG Usage Report (Generally Available) Multimodal support for AI functions in Fabric enables notebook users to apply AI capabilities directly to their unstructured data—including PDFs, images, and text files. With just a few lines of code, users can perform tasks such as summarization, classification, sentiment analysis, and more, all within their existing workflows. This capability is designed to work across both pandas and Spark, making it easy to bring AI-driven insights to a wide range of data science and analytics scenarios in Fabric. Figure: Load files into a table or classify insurance claim images with multimodal AI functions Check out the multimodal AI functions documentation to learn more. AutoML in Fabric (Generally Available) AutoML delivers a fully production-ready, low-code machine learning experience. In addition to the core AutoML capabilities powered by FLAML—automated model selection, feature engineering, and hyperparameter optimization—the end-to-end UI experience, making it easy to configure experiments, monitor training progress, compare models, and deploy the best performer directly from the interface. With integrated experiment tracking, reproducibility, and seamless deployment workflows, teams can confidently move from raw data to high-quality predictive models faster—while maintaining transparency, governance, and control within Fabric. Figure: AutoML includes the fully integrated UI experience for configuring experiments, tracking model performance, and deploying models end to end Check out the AutoML in Fabric documentation to learn more. Data Warehouse Fabric Data Warehouse recovery (Preview) In fast moving production environments, an item gets dropped accidentally due to an incorrect script. Suddenly, critical reports are broken, and teams are asking the same question: “How fast can we recover?” With dropped warehouse recovery in Microsoft Fabric, a deleted warehouse no longer means starting over. You can now restore a dropped warehouse together with everything that goes with it—data, schemas, snapshots, permissions, and saved queries—in minutes, without rebuilds, re‑ingestion, or complex restore workflows. Figure: Warehouse recovery in action—from drop to restore in minutes There’s no need to recreate environments, rerun pipelines, or scramble through backups. Recovery is simple, predictable, and designed to bring your warehouse back exactly as it was before the drop. This capability is built for the realities of modern analytics: rapid iteration, frequent deployments, and shared production environments. Instead of turning accidental deletes into prolonged outages, Fabric makes recovery a routine, low-stress operation. No panic. No need to rebuild. Just built in resilience—designed for real-world production analytics. To learn more, refer to the manage workspaces documentation. Alerts and actions Microsoft Fabric Data Warehouse provides operational intelligence closer to the data by integrating SQL queries with Fabric Activator rules. Traditionally, identifying an issue in query results is only the first step. Teams then need separate systems or manually follow up to notify the right people and act. With this integration, Data Warehouse makes it possible to define rules directly from SQL query outputs, so changes in data can trigger alerts and downstream actions automatically. This unlocks a simpler way to monitor business-critical conditions using familiar SQL workflows. Teams can create queries that detect scenarios such as SLA risks, failed processes, unusual trends, or threshold breaches, then attach rules that evaluate results continuously and respond in real time. The result is a more proactive analytics experience, where insights move beyond the warehouse and are acted on immediately. Figure: Create rules on SQL query results to detect data issues, monitor KPIs, and automatically trigger alerts or Fabric workflows. Analyze unstructured text using T-SQL AI functions (Preview) Microsoft Fabric Data Warehouse extends modern analytics beyond structured and semi‑structured data by introducing built‑in AI functions for working directly with the unstructured text. Traditionally, processing free‑form content such as notes, logs, or comments requires external services or complex pipelines. With these new capabilities, Fabric Data Warehouse enables text extraction, classification, sentiment analysis, and transformation directly in T‑SQL language, allowing data engineers and analysts to keep AI‑driven text processing inside the warehouse. The new AI functions simplify common text analytics scenarios using familiar SQL patterns. You can extract structured insights from unstructured text, analyze sentiment in feedback or messages, and classify content such as application logs or incident reports using contextual understanding rather than fragile rules or expressions. Fabric Data Warehouse also supports text transformation scenarios, including summarization, grammar correction, and translation, making it easier to standardize and enrich text data as part of existing data preparation workflows. The following visual is example of processing unstructured text in the Comments table: Figure: Analyzing comment text with AI functions This query enriches each user's comment by determining its sentiment; labeling the type of feedback or intent; and extracting key discussion signals such as the main topic, user intent, and any requested action using built-in AI functions. For advanced scenarios, Fabric Data Warehouse enables custom prompt-based processing through a generic ai_generate_response(instructions, text) function. This function allows teams to define precise transformation or extraction rules as prompts; apply domain specific logic; and reuse AI behavior consistently across queries and pipelines. Together, these capabilities significantly broaden the scope of data warehousing scenarios supported in Fabric, unlocking new ways to analyze and operationalize unstructured text using T-SQL. Refer to AI functions in Fabric Data Warehouse to learn more about analyzing text with built-in AI functions. ANY_VALUE aggregate Fabric Data Warehouse provides the ANY_VALUE() aggregate, which lets you return an arbitrary value from each group in T-SQL query. This is especially useful when you need to group results by a key (such as GeographyID) but you still want to project descriptive attributes (such as city name, and country) that are functionally the same for every row in that group. An example of such a query is illustrated in the following picture, demonstrating how ANY_VALUE() aggregate can be used to get the values from the group that are not changing. Figure: Using ANY_VALUE() to project descriptive columns while aggregating trips by GeographyID In this pattern, city, state, and country don’t add meaning to the aggregation because they’re constant for a given GeographyID. Adding these columns in the GROUP BY clause or applying more complex or costly aggregates like MIN or MAX is unnecessary overhead and makes queries harder to read and maintain. ANY_VALUE keeps the grouping logic minimal and the intent clear: aggregate by the key and simply carry through the descriptive columns. Refer to the ANY_VALUE function in Fabric Data Warehouse documentation to find additional scenarios where it helps simplify grouping and aggregation logic. Fabric warehouse custom SQL pools (Preview) Custom SQL pools for Fabric Data Warehouse gives administrators finer-grained control over how SQL compute resources are allocated across workloads. Custom SQL pools build on the warehouse’s autonomous workload management by letting you define your own isolation boundaries, explicitly assign resources, and route queries based on application context. With Custom SQL Pools, you can create multiple isolated SQL pools within a single workspace and allocate a percentage of available compute to each. Queries are routed to the appropriate pool ensuring that critical workloads get the resources they need without being impacted by other activities in the warehouse. Figure: Custom SQL Pool Configuration Key benefits include: Predictable performance for critical workloads—Reserve compute for business‑critical reporting or dashboards, so they aren’t disrupted by ad‑hoc queries or background processing. Flexible workload isolation without added complexity—Allocate resources where they matter most without needing to split workloads across multiple workspaces or scale capacity just to protect one workload. Custom SQL Pools are especially useful when multiple applications share a single Fabric warehouse or SQL analytics endpoint and have different performance or priority requirements. As your capacity scales up or down, your pool allocations automatically scale with it, preserving the relative resource distribution you’ve defined. Learn more about custom SQL pools in Fabric Data Warehouse: Custom SQL Pools - Microsoft Fabric | Microsoft Learn. SQL Audit Logs (Generally Available) SQL Audit Logs for Fabric Data Warehouse enable organizations to capture and analyze database activity for security monitoring, compliance, and forensic analysis. Figure: Configuring SQL Audit Logs With this release, we are expanding support and improving accessibility: Support for SQL Analytics Endpoint auditing. Direct access to audit files stored in OneLake, through OneLake Explorer. Ability to download or copy audit files through OneLake Explorer. Ability to open the .xel audit files directly in SQL Server Management Studio (SSMS) for deeper investigation. These capabilities make it easier for security and compliance teams to perform detailed investigations, long-term retention, and external analysis of workflows. For configuration steps and usage details, see the documentation: SQL Audit Logs in Fabric Data Warehouse - Microsoft Fabric | Microsoft Learn COPY INTO and OPENROWSET support for OneLake sources (Generally Available) Previously, this capability supported Lakehouse sources only. With this release, we are expanding support to all OneLake items (except Warehouses). This enables much more flexible ingestion scenarios, including: Using partner workloads such as COPY Jobs. Using staging areas across different Fabric items. Loading data stored anywhere in OneLake-backed items. Customers can now leverage OneLake as a unified staging layer for ingestion workflows while maintaining a consistent SQL experience. Figure: Executing COPY INTO from OneLake sources For full usage examples and configuration guidance, see the documentation: Ingest Data into Your Warehouse Using the COPY Statement - Microsoft Fabric | Microsoft Learn COPY INTO (Transact-SQL) - Azure Synapse Analytics and Microsoft Fabric | Microsoft Learn Outbound Access Protection (OAP) support for Warehouse (Generally Available) Outbound Access Protection for Fabric Data Warehouse provides stronger data exfiltration protection for enterprise environments. Warehouse now supports connector rules that allow organizations to control which external sources the warehouse can access. Customers can define rules to allow access to: Specific Azure Data Lake Storage Gen2 accounts Other Fabric workspaces Approved external connectors This expands the model introduced during Preview, where access was limited to OneLake and local workspace sources only. With connector rules, organizations can enforce controlled and auditable outbound connectivity, helping meet strict governance and compliance requirements. Figure: Supporting OAP Data Connection Polices For details on configuring connector rules, see the documentation: Workspace outbound access protection for data warehouse workloads - Microsoft Fabric | Microsoft Learn Full query text available in Query Insights Query Insights now show the full SQL query text, removing the previous 8,000‑character truncation. The complete query text is available in: The command column of queryinsights.exec_requests_history. The Query Details pane in the Query activity tab. This makes it significantly easier to understand what ran, especially for large, auto‑generated queries from BI tools, ORMs, or complex workloads. You can now retrieve the full query text directly using: SELECT distributed_statement_id, submit_time, total_elapsed_time_ms, command FROM queryinsights.exec_requests_history ORDER BY submit_time DESC; This query takes the most frequently executed query (from Frequently Run Queries) and pulls every historical execution with the full, untruncated SQL text, making it easy to understand exactly what is running and how often: WITH TopQuery AS ( SELECT TOP 1 query_hash FROM queryinsights.frequently_run_queries ORDER BY number_of_runs DESC ) SELECT erh.query_hash, erh.distributed_statement_id, erh.submit_time, erh.total_elapsed_time_ms, erh.status, erh.allocated_cpu_time_ms, erh.data_scanned_remote_storage_mb, erh.command AS full_query_text FROM queryinsights.exec_requests_history AS erh JOIN TopQuery AS tq ON erh.query_hash = tq.query_hash ORDER BY erh.submit_time DESC; Because the full statement is preserved, users can: Immediately understand what logic was executed, not just which query ran. Compare query text across executions to detect subtle changes or regressions. Correlate performance issues with specific joins, filters, or aggregations. This enhancement removes a major gap between observability and action, making Query Insights a more complete tool for day-to-day production troubleshooting. Live connectivity in Migration Assistant for Fabric Data Warehouse (Preview) The live connectivity in Migration Assistant for Fabric Data Warehouse lets you migrate object metadata by connecting directly to your source system into a new Fabric warehouse. This helps you accelerate migration and reduce upfront prep by eliminating the need to generate and upload a DACPAC for the metadata step. The object metadata of schemas, tables, views, functions, and stored procedures gets migrated to warehouse. Figure: Migration using direct connection to the source system Learn more about Migrate with a Direct Connection. Simplify data access with data sources (Generally Available) Fabric Data Warehouse lets you define external data sources that act as named references to locations in your lake (for example, a root folder of a Fabric Lakehouse or an Azure Storage account). External data sources (Preview) were introduced in preview in October 2025, now they are generally available and fully integrated into the Fabric experience, with the full IntelliSense and Copilot support in the SQL query editor. The following visual shows how to create a reusable reference to the Fabric Lakehouse root folder that represents a landing zone where you can store the files before ingesting them in warehouse: Figure: Creating external data source in Fabric Data Warehouse. In the following visual, you can see how you can query files using short, relative paths that are resolved against the data source root: Figure: Accessing files in the referenced data source using the relative path Using a data source keeps queries clean and portable. You can write easy to remember relative paths (like /Files/bronze/logs/*.jsonl) instead of embedding long, environment-specific URLs everywhere in the code. This makes scripts simpler to maintain and easier to share across workspaces and environments. Find more examples about the reading files form the lake in OPENROWSET(BULK) (Transact-SQL) documentation page. Real-Time Intelligence Business Events in Microsoft Fabric (Preview) With Business Events, organizations can move from observing what happened to acting on what matters, in real time. It enables organizations to respond faster, operate more intelligently, and scale real-time decision making across analytics, automation, and AI. You can generate business events from user data functions (UDFs) and notebooks. Once generated, a single business event can power multiple downstream actions, such as: Trigger alerts and automations with Activator, responding immediately via email or Teams. Execute custom logic using user data functions, reacting programmatically to business events. Run analytics and workflows in notebooks, using events to drive downstream analysis. Provide real‑time context with AI and ML, enriching models with governed business signals. Integrate with Spark jobs, dataflows, and Power Automate, enabling distributed processing and business process automation. With Business Events in Real-Time Hub, you can explore, define, and act on critical business signals for the whole organization in a unified experience. Figure: Business Events creation experience For more information about this feature, please refer to the documentation: Business Events in Microsoft Fabric. Building event-driven, real-time applications on database changes with Fabric Eventstreams Deltaflow (Preview) Building intelligent systems that react quickly to operational database changes is simpler with this update. With the release of DeltaFlow, Fabric Eventstreams can seamlessly capture inserts, updates, and deletes from operational databases, transform them from their raw Debezium format, and make them available to downstream event-driven applications using Activator and for real-time analytics in Eventhouse. There’s no need for custom Debezium/JSON processing code or to explicitly manage destination tables through source table schema changes. Easily connect to, ingest from, and transform raw CDC feeds into analytics-ready form. to_connect_to_a_CD Figure: Enabling DeltaFlow when connecting to an Azure SQL database Detect, fetch and register source database & table schemas in the Eventstream schema registry as they evolve. Figure: Automatic registration and use of source table schemas Automatically manage tables in analytics store as they continuously evolve with source schema changes without breaking pipelines. Figure: Automatically created Eventhouse tables with analytics-ready shapes For more information about these features, please refer to the document Building real-time, event-driven applications with Database CDC feeds and Fabric Eventstreams DeltaFlow (Preview). Real-time stream processing with Fabric Eventstreams and Spark notebooks (Preview) This update brings together Fabric Eventstreams and Spark Structured Streaming, making it easier for Spark developers and data engineers to work with real-time data in Microsoft Fabric. These enhancements enable you to access streaming data in Eventstreams directly from Spark notebooks, supporting low-latency processing and end-to-end real-time AI pipelines. Easily discover Eventstreams and real-time sources available through the Real-Time Hub, right from within Fabric notebooks. Figure: Real-Time Hub view inside Fabric notebook—discover Eventstreams in seconds Connect to and process streaming data within minutes using auto-generated PySpark code snippets. Figure: Auto-generated PySpark snippet in a Fabric notebook for an Eventstream Load and use existing notebooks from the Fabric Eventstreams portal. Figure: Load a Spark notebook as an Eventstream destination—reuse and collaborate Securely connect to any Eventstream from a Fabric Spark job without connection strings and secrets, using the enhanced Spark adapter for Eventstreams. Securely connect to any Eventstream from a Fabric Spark notebook using the enhanced Spark adapter—without connection strings or secrets, and with built-in auto-retry support. For more information about these features, please refer to the blog post Bringing Together the world of Real-time Intelligence and Spark Structured Streaming (Preview). Anomaly Detector full-item experience Introducing a refreshed Anomaly Detector full‑item experience that makes it easier to create, run, and explore anomaly detection workflows from end to end. Instead of working through disconnected steps or modal flows, you now get a single, full‑page canvas that brings configuration, analysis, and results together in one place. The updated layout follows Fabric’s shared item experience, so navigation and interactions feel consistent with the rest of the platform. With this new experience, you can more quickly define detection scenarios, run analyses, and immediately dig into detected anomalies without losing context. Clearer sectioning and action cues guide you through the process—from selecting signals and parameters to reviewing anomaly trends and individual data points. Results stay visible alongside configuration, making it easy to iterate, compare outcomes, and refine your setup in real time. Figure: An updated Anomaly Detection UI that simplifies setup, visualizes trends and anomalies in real time, and highlights high‑confidence events for faster investigation The full item Anomaly Detector experience also sets the stage for deeper investigation and richer insights. By consolidating analysis and results into a unified view, you can spend less time navigating and more time understanding what’s changing in your data. Whether you’re monitoring operational metrics or exploring unexpected behavior in real-time signals, these improvements help you move from detection to insight faster and with greater confidence. Operations agent playbook improvements and messages This month sees several improvements for operations agents’ ability to monitor your data and take actions. Based on usage and feedback we’ve heard so far, operations agents are better at mapping between your instructions and the fields in the Eventhouse you connect them to. You’ll also see they can build different types of rules to monitor the specific conditions in your data, including comparing string values in the data and counting data points over time. Finally, you’ll also see better messages if the agent can’t generate a playbook based on the data, goals, and instructions you’ve configured. In those cases, the LLM will try and describe the issue for example not being able to ground a field it inferred from your instructions to a field in the Eventhouse KQL database, or the parameters for a condition or action not being clear in the instructions. For example: This makes it easier for you to debug and unblock the agent configuration. Finally, we’ve updated our best practices and sample for how to give guidance and steering to the operations agent for it to follow your instructions. Learn more about this in the Operations Agent Best Practices and Limitations documentation. Live update for Real-Time Dashboards Real-Time Dashboards now support Live update, a feature that automatically refreshes dashboard visuals when new data is ingested into your underlying data sources. Instead of relying on fixed-interval refresh - which polls your data source on a set schedule regardless of whether new data exists - Live update uses a lightweight background query to detect when data arrives and triggers a refresh only when needed. This event-driven approach offers several benefits. Your dashboards stay current without the compute overhead of constant polling, making it particularly valuable for high-frequency data monitoring scenarios where you need to see data the moment it arrives. For organizations running multiple dashboards or monitoring large data volumes, Live update reduces compute load by eliminating unnecessary refresh cycles during quiet periods. Dashboard viewers also gain flexibility with the ability to pause live updates temporarily. If you're investigating a specific data point and don't want the visuals to change, you can pause updates to analyze the current state without interruption, then resume when you're ready to return to real-time monitoring. Dashboard editors can enable Live update through the dashboard settings, with configuration options including Live update (recommended), manual update only, or a fallback refresh interval for visuals that don't support ingestion detection. Learn more about configuring Live update for your dashboards, with the What is Real-Time Dashboard? documentation. Eventstream SQL Operator (Generally Available) During preview, the Eventstream SQL Operator introduced SQL-based stream processing in Fabric, enabling customers to transform live event data using familiar SQL with rich authoring, preview, and debugging capabilities. Write to multiple destinations from a single SQL operator Consolidate your real-time processing logic into one streamlined SQL block and route results to multiple destinations in a single step. This simplifies pipeline design, making it more efficient and lowers operational overhead. The updated authoring experience makes it easy to add multiple destinations directly within the SQL editor and preview results for each output independently. During testing, dedicated output previews let you validate transformations before you publish. Figure: Route data to multiple destinations from one SQL operator. Event ordering and late event arrival handling Configure event ordering policies directly within the SQL operator to handle late-arriving and out-of-order events. Define thresholds for how long to wait for delayed data and ensure accurate, event-time–correct processing—even in the presence of network delays or asynchronous producers. These policies help build more resilient real‑time pipelines that reflect how data behaves in the real world—not just in perfect conditions. Learn more about Fabric Eventstream SQL Operator. Together, these enhancements make Eventstream SQL Operator more powerful, more intuitive, and ready for production‑grade real‑time workloads. Anomaly Detection as a source in Eventstream Anomaly Detection can be added as a source in Fabric Eventstream, allowing you to publish anomaly events directly into your Eventstream for processing and action. You can add Anomaly Detection as a source from either Eventstream or Real-Time Hub. You can enrich your anomaly events by adding business context and additional information. Further, you may route real-time events to downstream workloads for automated alerting and dashboard visualization. Where to Add This Source You can add Anomaly Detection as a source in two ways: From Eventstream – Create a new Eventstream, select Anomaly detection events as a source. Figure: Adding Anomaly Detection source within Eventstream From Real-Time Hub – Navigate to Real-Time Hub, find the Fabric events and select Anomaly detection events. Figure: Adding Anomaly Detection events in Real-Time Hub Once added, anomaly events flow seamlessly into your Eventstream, ready for transformation and routing to downstream workloads. Get Started Anomalies are now another streaming event—ready to be transformed, enriched, and acted upon. Try out Anomaly Detection as a source in Eventstream today and unlock the power of real-time anomaly pipelines. Learn more about this in the Operations Agent Best Practices and Limitations documentation. Data series colors for real-time dashboard visuals When monitoring operational data, color choices matter. A status indicator showing “Critical” in red and “Healthy” in green communicates meaning instantly—viewers can interpret the visual without reading legends or labels. With data series colors, you can make these intentional choices rather than accepting system defaults. To configure data series colors, switch to Editing mode, select the Edit icon on your tile, and expand the Data series colors section in the Visual tab of the formatting pane. From there, you can select a color for each data series in your visual. Figure: Visual formatting: data series colors setting Figure: Color palette for setting visual elements colors Learn more about customizing your Real-Time Dashboard visuals, refer to the Customize Real-Time Dashboard visuals documentation. Use Copilot to create visuals in real-time dashboards (Preview) Dashboard editors can now use Copilot to create and edit visuals in Real-Time Dashboards using natural language. When you're in Edit mode, open the Copilot pane while creating a new tile or editing an existing one. Describe the insight you need - for example, "Show me the top 10 repositories by push events this week" - and Copilot generates the KQL query, returns the data, and suggests a visual that fits your results. Figure: Real-time dashboard visual in edit mode after Copilot answer has been applied You can accept Copilot's suggestion, refine your question with follow-ups like "Group by event type" or "Filter to the dotnet organization," or edit the query directly. Once you're satisfied, add the visual to your dashboard and use the no-code formatting options to customize its appearance. To learn more, refer to the Copilot-assisted real-time data exploration documentation. Instantly run and preview functions in Microsoft Fabric Eventhouse: no code required (Preview) Previously, working with an Eventhouse function involved manually writing KQL queries. You needed to enter the function name, provide parameters in the right format, and execute the query just to see what results you would get. If you wanted to view the function's body or metadata, you had to run a separate command. That's no longer the case. With the new Preview Functions capability in Microsoft Fabric Eventhouse, you can open the function definition, run the function, and instantly preview its results, with no manual KQL, no parameter guesswork, and no extra commands. Why this matters Eventhouse functions are powerful, but environments evolve. Databases grow, teams change, and you often inherit functions you did not write. Instead of guessing what a function does or manually building a query just to test it, you can: View the function definition instantly. Run the function and preview results with a single click. Test parameterized functions interactively. Browse your function list with search and sorting. This removes friction from everyday workflows. Whether you are exploring unfamiliar logic, validating outputs before building reports, or troubleshooting unexpected results, you get clarity in seconds instead of minutes. How to view or preview a function: In DB Explorer, expand Functions and select a function. A read-only version of the function opens. Select Preview results to instantly run the function and see the output. If the function has parameters, enter your values and preview the results based on your input. The preview shows up to 100 records, providing a quick snapshot of the function’s output. Figure: DB Explorer with the Functions folder expanded and a function selected. The function opens in read-only mode, and the Preview results option is available to run the function and display the output, with fields provided to enter parameters You can view a complete list of all stored functions, including their folder, description, and optional sorting. Built-in search makes it easy to find specific functions, making navigation and discovery simple even in large databases. Figure: Functions list with all available functions in the database, with options to sort, search, and open a menu with additional actions The new Run & Preview Functions feature in Microsoft Fabric Eventhouse lets you instantly inspect function definitions and preview results without writing KQL or handling parameters manually. Quickly explore, test, and manage all your stored functions in one place, saving time and reducing friction. Learn more with the Stored functions list documentation. Workspace monitoring dashboard templates in Microsoft Fabric Eventhouse (Preview) Fabric workspace monitoring provides rich telemetry across your workspace assets, including Eventhouses, Power BI Semantic Models, Data Engineering (GraphQL), and Mirrored Databases. The workspace monitoring data is stored in an Eventhouse, part of Fabric Real-Time Intelligence. To help you turn this data into actionable insights, we have created ready-to-use real-time dashboard templates with out-of-the-box visualizations. Currently, two templates are available: one for Eventhouse items and one for semantic models. From any Workspace Monitoring Eventhouse, users can create these dashboards directly. To get started, go to your Workspace Monitoring Eventhouse, open the upper ribbon, and select Fabric Monitoring. Figure: The ribbon in the Workspace Monitoring Eventhouse allows you to create out-of-the-box dashboards for monitoring From there, choose to create: Eventhouse Monitoring Dashboard—to monitor Eventhouse items in your workspace. You can track: Ingestion results and logs. Commands and queries monitoring. Metrics related to Eventhouse performance. Semantic Model Monitoring Dashboard—to monitor Semantic Models in your workspace. You can use semantic model logs to: Identify periods of high or unusual Analysis Services engine activity by capacity, workspace, report, or user. Analyze query performance and trends, including external DirectQuery operations. Track semantic model refresh durations, overlaps, and processing steps. Monitor custom operations sent using the Premium XMLA endpoint. Once created, the dashboards are ready to use immediately, giving you instant visibility in your workspace. You can also customize them to fit your specific needs. These templates make it fast and easy to track, analyze, and act on workspace activity, all in one place. Learn more in the documentation: Visualize your workspace monitoring . Databases Database Hub in Fabric The Database Hub in Fabric is a new unified database management experience that brings databases across edge, on‑premises, cloud, and Fabric into a single, coherent view. It provides teams with one place to explore, observe, govern, and optimize their entire database estate. Built for scale, the Database Hub uses agent-assisted intelligence to continuously reason over estate-wide signals, surface what changed, explain why it matters, and guide teams toward the right next actions. With built-in observability, delegated governance, and Copilot-powered insights, database agents help teams move from insight to action faster, while humans remain firmly in control of goals, boundaries, and trust. The result is a simpler, more confident way to manage databases at scale today, and a foundation for increasingly autonomous, intelligent database operations over time. Sign up for early access. SQL database in Fabric Since reaching general availability in November 2025, SQL database in Fabric has seen rapid customer adoption as organizations modernize SQL workloads with less operational overhead and tighter integration with analytics and AI. Guided by customer feedback, the platform emphasizes simplicity, autonomy, security, and AI optimization. We are introducing a set of improvements and new features that make it easier to migrate, manage, and optimize SQL workloads in Fabric: Simplified migration with new assistant: The Migration Assistant in public preview helps SQL developers move SQL Server and Azure SQL workloads into Fabric by importing schemas, assessing compatibility, and guiding migration with minimal manual effort. Configurable autonomous management: While maintaining a SaaS-first approach, new options allow database-level control over vCore scaling, expanded compatibility levels, enhanced T-SQL features, and settings that ease application transitions without code changes. Support for all collations: All Azure SQL database collations are supported when creating a new SQL database in Fabric for enhanced global data compatibility and app development flexibility. Collations control text sorting and comparison in SQL databases, impacting filtering, searches, and multilingual content management. Users can specify collations seamlessly during database creation via the REST API across deployment methods. Check out the How to set a different collation for SQL database in Fabric demo and explore the sample code in Git repo. Enhanced data mirroring and security: Auditing and Customer Managed Keys are generally available. CMK for Fabric SQL lets you encrypt databases with your own Azure Key Vault keys to gain full control over key ownership, access, rotation, and compliance. Users can selectively manage which tables are mirrored to OneLake for immediate analytics access. AI and monitoring integration: SQL database in Fabric supports vector search with DiskANN and integrates with Azure AI Foundry for advanced semantic search and AI scenarios, alongside workspace performance dashboards for unified monitoring and optimization. Enhanced data recovery: In Fabric, when a database is deleted, it goes into a soft-deleted state into the Fabric Workspace’s Recycle Bin tab. Depending on the retention configured, the deleted database can be recovered from the Recycle bin while in retention. In addition to this Recycle bin experience, the Fabric SQL database also has the backup retention period configurable from 1-35 days. When the database is hard deleted from the Recycle bin, the backups are still available for the configured backup retention period. This new improvement allows you to restore the backup into a new database to any point in time within the restorable period. Cosmos DB mirroring with Private Link and VNET Cosmos DB mirroring with Private Link and VNET enables customers to mirror data from privately secured Azure Cosmos DB accounts into OneLake. This allows organizations to maintain consistent network security and compliance while supporting near‑real‑time analytics and AI workloads in Microsoft Fabric—strengthening Fabric’s enterprise readiness by design. Figure: Mirroring data from Azure Cosmos DB accounts secured with Private Endpoints or VNETs into OneLake To learn more, refer to the Cosmos DB Fabric Mirroring for Private Networks documentation. Data Factory—Copy Job Richer Change Data Capture (CDC) with Oracle, Fabric DW, and SCD Type 2 Copy job continues to improve the no‑code CDC experience with richer, enterprise‑ready replication patterns. This release introduces a set of enhancements in Copy job in Microsoft Fabric Data Factory that make CDC replication more powerful and easier to use without writing code: Oracle CDC source—Capture changes directly from Oracle databases. Fabric Data Warehouse sink—Replicate CDC data into Fabric Data Warehouse. SCD Type 2—Preserve full history with valid dating, and handle deletes as soft deletes. With built‑in SCD Type 2 and soft delete handling, Copy job automatically preserves every version of a record as it changes over time, instead of overwriting history. This makes it easy to answer point‑in‑time questions, support regulatory audits, and run accurate historical analytics—capabilities that traditionally require complex MERGE logic or custom code. Figure: Enabling SCD Type 2 in Copy job with One Click Learn more in the Change data capture (CDC) in Copy Job documentation. Every row is traceable with built-in audit columns Audit columns are additional metadata columns that Copy job can automatically append to every row it writes to the destination. These columns don't come from your source data—they're generated by the platform to describe the data movement itself. When you enable audit columns in Copy job, each row in your destination table can be enriched with information such as: Audit Column What It Captures Data extraction time The timestamp when the row was extracted from the source by a Copy job run File path The source file path the row was read from (applicable for file-based sources) Workspace ID The Fabric workspace ID where the Copy job resides Copy job ID The unique identifier of the Copy job item Copy job run ID The unique identifier of the specific Copy job execution Copy job name The name of the Copy job that moved the row Lower bound The lower bound value of the incremental window for the current run Upper bound The upper bound value of the incremental window for the current run Custom A user-defined static value—add any additional context your team needs. For example, you can add your source server name here Table: Audit column list With audit columns enabled, you can answer the following questions for any row in your destination table: When was this data extracted? Exact timestamp from when the row was read from the source. Where did it come from? Which file path, which data store. Which job moved it? Which Copy job from which Workspace, which specific run, by name and ID. What was the incremental scope? Lower and upper bounds tell you exactly what slice of data this run covered. No custom code. No expression authoring. Add as many audit columns as you want, and every row in every table your Copy job writes will include this metadata automatically. Figure: Setup audit column in Copy job. Figure: Output on destination data Learn more in What is Copy job in Data Factory - Microsoft Fabric. Workspace Monitoring for Faster, Scalable Troubleshooting As Copy jobs scale from a handful to hundreds, visibility becomes critical. Fabric Workspace Monitoring brings centralized, log‑level observability to Copy job executions, streaming detailed run data into a query able Monitoring Eventhouse inside your workspace. Teams can analyze failures, throughput, duration, and data volumes across all Copy jobs in one place—without inspecting jobs individually. With historical logs, cross‑item correlation, and integration with Data Activator for alerts, Workspace Monitoring helps DataOps teams detect issues earlier and troubleshoot faster at scale. Figure: PBI Report against Fabric Workspace Monitoring metric from Copy job Learn more in Workspace Monitoring for Copy Job in Microsoft Fabric - Microsoft Fabric Boost performance automatically with AutoPartitioning Moving large tables efficiently often requires careful partition tuning—but Copy job now does this automatically. With auto‑partitioning, Copy job detects large datasets and applies an optimal parallel read strategy without any manual configuration. This delivers dramatically higher throughput out of the box, whether you’re copying millions or hundreds of millions of rows. The system adapts dynamically based on data size and source characteristics, ensuring consistent performance across tables while eliminating per‑table tuning effort. Figure: Enabling auto partitioning in Copy job Learn more in What is Copy job in Data Factory More flexible incremental copy with new watermark column types Incremental copy is a core pattern for keeping analytics data up to date—but in real world systems, changes aren’t always tracked with a clean datetime column. To address this, Copy job now supports additional watermark column types, making incremental copy more flexible and applicable across a broader range of source systems Copy job now supports ROWVERSION, Date, and String (interpreted as datetime) watermark columns. This allows you to choose the column that best represents change in your source system, while Copy job continues to automatically manage state, checkpoints, and incremental windows. ROWVERSION enables precise and reliable change tracking in SQL‑based systems, capturing every insert and update without relying on application‑managed timestamps. Date watermark support works seamlessly with common columns like LastUpdatedDate or ModifiedAt, with built‑in delayed extraction to prevent data loss or overlap between runs. String (interpreted as datetime) support removes the need for custom queries or schema changes when timestamps are stored as strings, improving compatibility with real‑world schemas. These enhancements make incremental copy easier to configure, more resilient in production, and better suited for diverse enterprise data models—without adding complexity for users. Learn more in What is Copy job in Data Factory - Microsoft Fabric. Data Factory—Dataflow Gen2 Preview-only steps (Generally Available) This capability improves authoring performance without changing runtime behavior. Preview-only steps let you run specific transformations during data preview only, automatically excluding them from dataflow execution and refresh. That means your production logic stays exactly the same—while the authoring experience becomes faster, smoother, and more responsive. This capability addresses a common challenge when building Dataflow Gen2 items: iterating on logic can be slow when previews must evaluate full datasets. Preview‑only steps make it possible to temporarily reduce data volume or complexity during development, enabling faster validation of transformations without introducing conditional logic or modifying the final query definition. Common uses include filtering or isolating subsets of data to accelerate previews, testing transformation logic without waiting for full evaluation, and exploring new data sources while keeping production execution intact. Because preview‑only steps are ignored during refresh and run operations, they provide a safe way to optimize the authoring workflow without risking unintended changes in published outputs. Figure: The "Enable only in previews" option within the applied steps section Preview‑only steps are also integrated into specific authoring dialogs, including file system views and the Combine files experience. In these contexts, Dataflow Gen2 can automatically introduce preview‑only logic to limit sample data used during preview evaluation, further reducing load time while preserving the behavior of the final dataflow. With general availability, preview‑only steps become a standard part of the Dataflow Gen2 authoring model—helping teams iterate faster, validate transformations more efficiently, and maintain a clear separation between development‑time experimentation and production execution. Learn more: Preview only step in Dataflow Gen2 (Preview) - Microsoft Fabric. Fabric Variable Library integration (Generally Available) Throughout the last couple of months, we’ve removed some of the limitations such as: Variable limit: the previous limit was 50 variables. You can now reference as many variables as you need in your Dataflow. Power Query editor support and using a default value: you can now see how the variable gets evaluated within the actual Power Query editor. Not only that but within the Dataflow Gen2 experience, after enabling the input widgets through the Options menu, you will also have a new way that simplifies how you can reference variables with a complete no code experience fully integrated into the experiences and dialogs that you love from Dataflow Gen2: Figure: The Filter rows dialog within Dataflow Gen2 showing the input widget and the option to Select a workspace variable After selecting this option in any of the dialogs, the experience of selecting a Variable from a library will appear. Figure: The select variable dialog invoked from within a Dataflow Gen2 Be sure to test this improved experience and share your feedback. Learn more: Use Fabric variable libraries in Dataflow Gen2 (Preview) - Microsoft Fabric New data destinations Dataflow Gen2 continues to expand where curated data can land, supporting both lake‑first architectures and hybrid data estates. With new and updated destinations, teams can publish transformed outputs in the formats and platforms that best fit their downstream consumers—whether that’s open lake storage, lakehouse files, enterprise warehouses, or business‑friendly file formats. Azure Data Lake Storage Gen2 (Generally Available) Dataflow Gen2 now supports Azure Data Lake Storage Gen2 (ADLS Gen2) allowing teams to land curated outputs directly into their data lake using open formats and folder structures aligned to organizational standards. This enables lake‑first ingestion patterns for organizations that treat ADLS as their system of record, while still authoring transformations using low‑code Dataflow Gen2 experiences. Common scenarios include reusing curated outputs across Fabric (Spark and SQL) as well as external systems that are read directly from Azure Data Lake Storage Gen2. Figure: ADLS gen2 destination option Lakehouse files (Generally Available) Dataflow Gen2 can write outputs directly into the Files area of a Fabric lakehouse. This is useful when downstream consumers expect file‑based outputs rather than tables, or when teams need to align with existing folder and file conventions inside the lakehouse. This enables patterns where transformed extracts are consumed by Spark notebooks, pipelines, or external tools, while also supporting hybrid designs where some Dataflow outputs are tables and others are files within the same Fabric workspace. Figure: Lakehouse files option Snowflake databases (Preview) This enables transformed outputs to be published directly into Snowflake databases as part of Fabric‑based, low‑code transformation workflows. This supports hybrid data estates where Fabric is used for transformation while Snowflake remains the target platform for analytics or data sharing. This preview helps standardize transformations across platforms and enables analysts to departments to publish governed outputs into Snowflake without duplicating transformation logic. Figure: Snowflake destination option Excel files (Preview) Dataflow Gen2 is introducing the ability to write outputs as Excel files (Preview) for supported filesystem destinations such as SharePoint and ADLS Gen2. This makes it easier to support business processes that still rely on Excel, while keeping transformation logic centralized and governed in Fabric. Typical scenarios include publishing refreshed Excel extracts for operational reporting or legacy workflows, and standardizing Excel output formatting from a single Dataflow definition. Schema support in Fabric data destinations (Generally Available) As Dataflow Gen2 adoption grows, many teams run into organizational challenges when publishing tables into shared destinations. Without schema control, teams often resort to creating separate databases, warehouses, or lakehouses just to keep tables logically grouped—adding complexity and making collaboration harder. With this release, Dataflow Gen2 data destinations now support writing into specific schemas (where applicable). This capability is now generally available for destinations such as Fabric SQL databases, Lakehouses, and Warehouses, giving teams more control over how Dataflow outputs are structured and governed. What’s improved Better organization without extra destinations: Teams can organize tables by domain—such as finance, sales, or HR—using schemas instead of creating separate destinations for each area. This keeps environments cleaner while still enforcing logical separation. Smoother collaboration in shared environments: Multiple teams can publish tables into the same warehouse or SQL database while maintaining clear ownership and structure through schemas. This reduces naming conflicts and supports shared analytics models without friction. Figure: The connection settings for the Warehouse connector using the advanced options to set the Navigate using full hierarchy to true By aligning Dataflow Gen2 outputs with enterprise schema conventions, this enhancement makes it easier to support multiteam data platforms, improve governance, and scale Dataflow Gen2 usage across the organization without restructuring existing destinations. Learn more: Dataflow Gen2 data destinations and managed settings - Microsoft Fabric AI-Powered Prompt Transform (Generally Available) Fabric AI Prompt is integrating generative AI features into the low-code data transformation process. Authors can enrich and transform data using natural language prompts without building or managing machine learning models, while staying within the Dataflow Gen2 execution model. The AI Prompt capability is accessed from the Add column experience, where authors define a prompt and select columns to provide contextual input. This allows AI-driven enrichment to be expressed inline alongside existing Power Query transformations, keeping logic centralized and auditable. Figure: The AI Prompt dialog in Dataflow Gen2 Moving forward, all operations associated to the usage of AI Prompt within Dataflow Gen2 will be accounted towards an explicit AI meter with the operation name of “AI Functions”. Learn more: Fabric AI Prompt in Dataflow Gen2 (Preview) - Microsoft Fabric Publish experience UX + performance improvements (parallelized query validations) Publishing complex Dataflow Gen2 items can be time‑consuming, especially when dataflows contain many queries or multiple destinations. In these cases, validations are required before a dataflow can be published, and waiting for those checks to complete often slows down iteration and troubleshooting. With this release, we’ve improved the Dataflow Gen2 publish experience through a refreshed user interface and performance enhancements that parallelize query validations. By running validations concurrently, publish operations complete faster and surface issues earlier in the process. What’s improved Less time waiting: Dataflows with multiple queries and destinations publish faster, reducing idle time during validation and helping teams move through development and testing more efficiently. Clearer guidance during publication: Validation results are available sooner, making it easier to identify and resolve issues without repeated publish attempts or back‑and‑forth edits. Together, these improvements shorten the publish cycle, reduce friction when working with larger dataflows, and help teams iterate on Dataflow Gen2 solutions with more predictable and responsive feedback. Learn more: Dataflow Gen2 with CI/CD and Git integration. Save As Improvements: Scheduled Refresh Policies and Public APIs Save As continues to improve the migration experience to Dataflow Gen2 (CICD), especially for teams moving large numbers of dataflows across workspaces or tenants. One common challenge during migration is preserving refresh behavior—copied dataflows often require manual reconfiguration before they are production ready. With this release, Save As now supports Scheduled Refresh Policies for Dataflows Gen1, ensuring that refresh configurations are retained when copying a dataflow. This reduces post migration cleanup and helps teams move faster with fewer manual steps. In addition, we’re introducing a new public Save As API for Dataflows Gen1 designed for automation and bulk operations. This enables organizations to programmatically copy dataflows at scale, making it easier to support structured migration plans and repeatable rollout processes. What’s improved Streamlined refresh configurations: Dataflows created using Save As can now inherit scheduled refresh policies from the source dataflow, helping ensure consistent refresh behavior without re‑authoring schedules after migration. Automation at scale: The new Save As public API enables automated and bulk copy scenarios, allowing teams to migrate many Gen1 dataflows to Gen2 programmatically. This is particularly useful for multi‑workspace and multi‑tenant deployments where manual migration isn’t practical. Together, these enhancements reduce migration friction, minimize manual edits, and help teams adopt Dataflow Gen2 more efficiently—whether migrating a handful of dataflows or rolling out Gen2 at enterprise scale. Figure: Dialogs for the refresh and scheduling mechanism when using the Save as experience for Dataflow Gen2 Learn more: Save As Dataflow Gen2 documentation and public Save As API reference. SharePoint site picker in Modern Get Data and Data destinations (Preview) SharePoint Site Picker replaces manual URL entry with a browsable dropdown, so you can select the right SharePoint site directly instead of finding and pasting URLs Why this matters Eliminates manual URL copy-and-paste and context switching. Reduces connection errors caused by wrong URL formats. Surfaces Recent sites and Favorite sites instantly on dropdown open and enable you to search to find sites. Where the experience is available SharePoint site picker is available for SharePoint sources of Get Data in Dataflow Gen2, Pipelines, Copy Job, and Lakehouse shortcuts, and as a destination in Dataflow Gen2. How to use SharePoint Site Picker Simplified SharePoint Site Selection: Instead of copying URLs manually, use the Site URL dropdown to choose from Recent sites and Favorite sites. Figure: SharePoint Site Picker dropdown Quick Search Capability: Find related sites faster by typing in the dropdown search box. Once you select a site, you can load data into the Power Query editor for transformation. Figure: SharePoint Site Picker searched for results Learn more about SharePoint folder connector, SharePoint list connector, and SharePoint online list. Diagnostics download (Preview) Dataflow Gen2 diagnostics download provides a simple way to collect logs and diagnostic artifacts for both cloud-based and VNET gateway dataflows. Instead of rerunning refreshes or guessing at failures, you can download the information needed to investigate issues directly. This helps teams fail faster and fix issues sooner. Downloadable diagnostics make it easier to identify refresh failures, performance bottlenecks, and connectivity problems, including complex networking scenarios that rely on VNET data gateways. Figure: The recent runs dialog showing the new button at the bottom left of the dialog to Download detailed logs With clearer signals available upfront, support investigations are shorter and operational friction is reduced. Learn more: An overview of refresh history and monitoring for dataflows. Advanced Edit for destinations (Preview) The new Advanced Edit experience for Data Destinations enables editing of the underlying M logic that configures destination settings. This unlocks deeper customization, including the ability to leverage parameters to drive destination behavior—an important step for teams standardizing deployments across environments. Parameter-driven destinations: switch target schema/table, file paths, or naming conventions without rewriting queries. Unblock advanced scenarios that require destination settings not yet available in the simplified UI. Figure: The new Advanced editor for data destinations Learn more: Advanced edit for data destination queries in Dataflow Gen2 - Microsoft Fabric. Data destination validations during publish (Preview) Dataflow Gen2 now validates data destinations during publication, helping catch common issues earlier in the development cycle. These validations surface problems such as missing permissions, invalid destination settings, or naming conflicts before the first refresh runs. By shifting these checks to publish time, authors get clear, actionable errors sooner—when changes are easiest to fix. This shortens the feedback loop and reduces time spent troubleshooting runtime refresh failures after deployment. For creators, earlier validation means fewer broken dataflows entering production. This reduces operational noise, minimizes rework, and helps ensure that published dataflows are refresh ready and more stable by default. Learn more: Dataflow Gen2 data destinations validation rules. Evaluate query API (Preview) The Execute Query API (Preview) enables on-demand execution of Power Query logic in Dataflow Gen2 scenarios—without requiring a full scheduled refresh cycle. It’s designed for cases where you need to trigger transformations programmatically (or in response to events) and retrieve results quickly for downstream processing. Event-driven pipelines: run a transformation when new data arrives and push outputs to a destination or consumer immediately. Streaming and near-real-time scenarios: execute queries more frequently than a typical scheduled refresh to support operational dashboards and alerting workflows. Automation at scale: integrate with orchestration tools and scripts to run specific queries as part of broader ETL/ELT jobs. Faster debugging: re-run targeted queries to validate fixes without republishing the entire dataflow. Learn more: Execute Query API (Streaming) documentation (Preview). Data Factory Data Factory MCP (Preview) Dataflow Gen2 offers a suite of pipeline functions, including dataflow creation, M (Power Query) scripting, connection management, query execution, and refresh coordination. These tools are directly accessible to AI assistants. Access is available through platforms such as VS Code, Claude, ChatGPT, Gemini, or via the command line. Why it matters AI assistants create, test, and deploy dataflows through natural language—no browser tabs or manual configuration required. Iterative M development via execute_query lets the AI test transforms against live data before committing to a full refresh. MCP Apps provide guided UI forms (connection setup, gateway selection) inside the chat panel. Open source (GitHub), ships as a NuGet package, runs locally—credentials never leave your machine. Learn more: Data Factory GitHub repo. IBM Netezza ODBC Driver (Generally Available) As we move away from using the embedded Simba driver, customers now have a more dependable and supported option by using their own Netezza driver. This update ensures continued connectivity, long-term support, and a more future-ready experience for organizations using the Netezza connector. Customers do not need to install the new connector; you may reuse your existing connector but will need to install the new IBM Netezza ODBC driver. Figure: IBM Netezza Connector Selection in Fabric UI Reference the IBM Netezza ODBC documentation for more information Google BigQuery connector (Generally Available) This update reflects a shift to the newer GBQ connector as the supported, long-term path forward, providing customers with improved reliability, and alignment with our evolving security standards. With this update, customers can use a connector designed for durability, compliance, and future enhancements. Figure: Google BigQuery Connector in Fabric UI Additional details are available in the Google BigQuery connector documentation. QuickBooks Online connector retirement The QuickBooks Online connector is being retired and will no longer be supported as of March 2026. As part of our ongoing platform evolution, this change streamlines our connector portfolio and ensures our continued commitment to only the highest level of secure data connectivity. After retirement, customers will no longer be able to create new connections, and existing connections may no longer function. Lakehouse Maintenance activity in Fabric Pipelines (Preview) Keeping your Lakehouse healthy shouldn’t require a long checklist or manual scripts. The new Lakehouse Maintenance activity (Preview) makes it easy to automate common upkeep tasks directly inside Fabric Data Factory pipelines. Figure: The Lakehouse maintenance activity in Fabric pipelines With this activity, you can schedule and run actions like vacuuming old files, optimizing table layouts, and managing storage—all in a repeatable, governed workflow. It’s a simple way to keep performance high and storage costs in check, especially for teams managing large or fast‑growing datasets. Figure: The Lakehouse maintenance activity settings Whether you run maintenance nightly or as part of a broader DataOps process, this activity helps support reliable Lakehouse operations. Check out our Lakehouse Maintenance documentation. Refresh SQL endpoint activity in Fabric pipelines (Preview) The process of keeping your SQL analytics layer current is now simpler. The new Refresh SQL endpoint activity (Preview) lets you refresh your Lakehouse SQL endpoint on-demand or as part of your pipeline orchestration. Figure: The Refresh SQL endpoint activity You can trigger targeted refreshes after data ingestion, run coordinated refreshes alongside your transformations, or ensure downstream consumers always see the latest state. It’s built for operational consistency – especially for BI, reporting, and real‑time analytics scenarios that rely on predictable SQL performance. Figure: The Refresh SQL Endpoint activity settings. This activity gives you more control, less manual overhead, and a smoother end‑to‑end refresh experience. Check out the RSQL documentation for more details. Generate Pipeline expressions with Copilot (Generally Available) Writing expressions doesn't have to be time-consuming; simply describe your needs in natural language, and Copilot will generate pipeline expressions for you. Figure: Generate Pipeline expressions with Copilot Whether it’s building dynamic folder paths, conditional logic, string parsing, or parameterized values, Copilot now handles the expression authoring for you. This feature removes friction for both new users and power users – saving time, reducing errors, and making expression logic easier to understand. Workspace monitoring for Fabric Data Factory’s pipelines and Copy job (Preview) Operational observability continues to evolve in Fabric. We’re taking the first major step toward workspace‑level observability in Microsoft Fabric Data Factory. Until now, understanding how pipelines and copy jobs behave at scale often meant inspecting individual runs via Monitoring Hub. With the introduction of workspace monitoring (Preview), Data Factory begins a shift to a workspace‑wide view of operational health. The newest workspace monitoring updates bring clearer visibility and faster troubleshooting across your pipeline ecosystem. What’s available A workspace-wide view of item-level runs Rich filtering, sorting, and drilldown Insight into failure patterns, duration trends, and operational health Faster navigation—no need to click into each pipeline This gives DataOps teams a unified lens to understand performance and diagnose issues quickly. Figure: A view of your pipelines and Copy jobs within the workspace monitoring solution Coming Soon Activity-level L2 monitoring for pipelines. Copy job L2-level monitoring (Preview) for deeper insights and debugging. These improvements continue building toward a more comprehensive, intuitive monitoring experience for production workloads. Check out our docs on Enable Workspace Monitoring in Microsoft Fabric and Workspace Monitoring for Copy Job in Microsoft Fabric for more information on how to use this experience. Interval-based schedules The latest enhancement to Fabric Data Factory pipelines is the availability of interval-based schedules! This powerful new feature allows you to automate data workflows at regular non-overlapping intervals, like the popular tumbling window trigger in Azure Data Factory. Figure: Interval-based schedule configuration in Fabric Data Factory With interval-based scheduling, you can easily configure recurring pipeline runs that ensure timely data processing and seamless integration across your architecture. New Airflow APIs New Airflow Operators Apache Airflow jobs in Fabric Data Factory facilitate the execution of a wide range of Fabric artifacts through native operator integration. Users can run artifacts such as Notebooks, Spark job definitions, Pipelines, Semantic Models, and user data functions directly from their DAGs. Apache Airflow jobs now provide support for executing Copy jobs and dbt jobs! Figure: Airflow operators for Fabric items, including Copy job and dbt job execution To learn more, refer to Run a Fabric item using Apache Airflow DAG. PowerShell model for gateways (Generally Available) The PowerShell model for gateways now delivers fully supported, production-ready automation for gateway lifecycle, update, restore, and configuration management. This release introduces new commands for version discovery and upgrade control, along with reliability and usability improvements that make large-scale, script-driven gateway operations easier and more robust. Figure: Using the Gateway PowerShell module to manage gateway operations from the command line Learn more through the gateway PowerShell documentation and cmdlet reference on Microsoft Learn. Certificate and proxy support for VNet data gateway (Generally Available) Certificate and proxy support for VNet data gateway enables secure, compliant connectivity in enterprise environments. Organizations can use enterprise-issued certificates for gateway authentication and configure proxy routing when direct internet access is restricted. Together, these capabilities strengthen security, support corporate network policies, and expand deployment flexibility in controlled and regulated infrastructures. nshot_of_certificate_and_proxy_settings_for_a_Virtual_Network_Data_Gateway Figure: Configure certificate authentication and proxy for a Virtual Network Data Gateway Learn more through Manage virtual network (VNet) data gateways. Virtual network data gateway supports up to nine instances This update enables greater scalability and higher throughput for enterprise workloads. With expanded instance capacity, organizations can handle increased data movement and processing demands, improve parallel job performance, and enhance reliability for mission-critical tasks. This update provides more flexibility to scale gateway infrastructure in line with growing business needs. Figure: Virtual Network Data Gateway now supports scaling up to nine instances per cluster Learn more What is a virtual network (VNet) data gateway. SSIS Pipeline Activity (Preview) SQL Server Integration Services (SSIS) has been a cornerstone of enterprise data integration for decades, powering mission-critical ETL workloads across thousands of organizations worldwide. Invoke SSIS Package activity in Data Factory in Microsoft Fabric (Preview), provides the power of your existing SSIS investments directly into Fabric's unified SaaS analytics platform. Figure: Add an Invoke SSIS Package activity Many enterprises have significant investments in SSIS packages that orchestrate complex ETL workflows across on-premises databases, file systems, and cloud services. Until now, running these packages required either an on-premises SQL Server, or the Azure-SSIS Integration Runtime in Azure Data Factory. Both options meant managing additional infrastructure and staying outside the Fabric ecosystem. Figure: Invoke SSIS package activity configuration But the Invoke SSIS Package pipeline activity in Microsoft Fabric Data Factory changes this. It allows you to execute your existing SSIS packages directly from a Fabric pipeline, enabling true lift-and-shift of legacy ETL workloads into Fabric—no package rewrite required. There is no need for integration of runtime management or stopping and starting IRs; simply incorporate them into your pipeline. Seamlessly upgrade Azure Data Factory and Synapse pipelines to Microsoft Fabric (Preview) Microsoft Fabric Data Factory now offers a guided (Preview) migration experience to help you move existing Azure Data Factory (ADF) and Azure Synapse Analytics pipelines into Fabric—starting with an assessment-first approach so you can migrate intentionally and validate before switching production workloads. Review readiness and plan next steps: The assessment categorizes pipelines and activities so you can decide what to migrate now vs. what to fix or defer. You can also export results to CSV for offline review and remediation planning. Figure: Review pipeline and activity readiness results in Azure Data Factory (ADF only) Mount your factory to Fabric For Azure Data Factory migrations, you’ll mount your ADF into a Fabric workspace and then continue the remaining steps inside Fabric. Figure: Continue the migration flow in Fabric after mounting Migrate selected pipelines in Fabric In Fabric Data Factory, open the mounted factory (ADF) or chosen workspace (Synapse), then migrate the pipelines you want to migrate. Map linked services to Fabric connections and complete migration During migration, you’ll map ADF/Synapse linked services to Fabric connections. For guidance on creating and managing connections in Fabric, refer to Data source management. Figure: Map Linked Services to Fabric Connections Validate and promote After migration, validate connections and credentials, run end-to-end tests, and then re-enable triggers as needed. Pipelines migrate safely, with triggers disabled by default so you stay in control of execution. Learn more: Upgrade your Azure Data Factory pipelines to Fabric. Data Factory—Mirroring Mirroring for SAP (Generally Available) Built on top of SAP Datasphere’s Premium Outbound Integration, mirroring for SAP seamlessly integrates Fabric’s advanced mirroring engine with SAP Datasphere’s replication flows, unlocking connectivity through SAP’s native data extraction technologies. This means direct access to the full suite of SAP applications—whether it’s SAP S/4HANA (on-premises or cloud), SAP ECC, SAP BW, SAP BW/4HANA, or cloud solutions like SAP SuccessFactors, SAP Ariba, and SAP Concur. Mirroring capabilities allow you to: Eliminate data silos by bringing SAP data alongside other enterprise sources in OneLake. Maintain end-to-end data lineage and governance for compliance and auditability. Accelerate time-to-insight with near real-time data replication no custom ETL required. Figure: Mirrored database for SAP Learn more in Microsoft Fabric Mirrored Databases From SAP. Mirroring for Oracle databases (Generally Available) Mirroring for Oracle is now available in Microsoft Fabric, bringing a production‑ready, enterprise‑grade way to continuously replicate Oracle data into OneLake with no custom ETL pipelines. This milestone reflects strong validation from customers already running Mirroring for Oracle in production and marks a major step forward in Fabric’s zero‑ETL data integration strategy. With near real‑time data replication, customers can keep analytics, BI, and AI workloads continuously in sync with their operational Oracle systems. This release delivers improved stability, scale, and operational readiness, informed directly by customer feedback from public preview deployments. Mirroring for Oracle integrates natively with Fabric experiences like Power BI, Notebooks, and Lakehouses, enabling faster insights without disrupting existing Oracle workloads. As a fully supported capability, Mirroring for Oracle is now ready for broad enterprise adoption with long‑term investment from the Fabric platform team. Figure: Mirroring for Oracle creation steps Learn more at Mirroring for Oracle in Microsoft Fabric. Mirroring for Azure Database for MySQL (Preview) Mirrored databases now support Azure Database for MySQL. This capability enables you to directly replicate data from Azure Database for MySQL Flexible Server into Fabric in near real time, ensuring that information remains current, readily query-able, and seamlessly integrated throughout the analytics stack without the need for traditional ETL processes. Mirrored MySQL data is managed alongside other data sources, facilitating cross-source querying, unified reporting, and comprehensive analytics. Figure: Screenshot of configuring a mirrored database for Azure Database for MySQL Learn more in Microsoft Fabric Mirrored Databases for MySQL. Mirroring for SharePoint List (Preview) Mirroring for SharePoint Lists enables continuous replication of SharePoint Lists and Document Libraries into OneLake without building custom ETL pipelines. This capability keeps SharePoint data automatically synchronized in near real time, ensuring analytics in Fabric stay aligned while SharePoint remains the system of record. When mirrored, both list tables and document library metadata land in OneLake in an analytics‑ready format, with document libraries replicated via shortcuts and converted into Delta Lake tables. Figure: Mirroring setup for a SharePoint list Fabric automatically creates a mirrored database and a read‑only SQL analytics endpoint, providing a rich analytical surface over the replicated data. As changes are made in SharePoint—such as new columns or updated rows—those updates flow continuously into Fabric, keeping schemas and data in sync. This public preview unlocks a simple, unified way to analyze SharePoint operational data across Fabric workloads including SQL, Power BI, notebooks, and data engineering experiences. Extended Capabilities in Mirroring: Change Delta Feed and Snowflake Mirroring Support for Views (Preview) Optional enhancements that build on core mirroring to support more advanced, real‑world analytics scenarios. These capabilities are designed for customers who need more than basic replication—enabling faster freshness, incremental processing, and business‑ready data without building or maintaining complex ETL pipelines. Including Change Data Feed (CDF), which captures inserts, updates, and deletes at a granular level and applies them incrementally into OneLake, allowing mirrored data to stay continuously fresh without full reloads. Extended Capabilities also include Mirroring Views for Snowflake (with support for other sources coming soon), which replicate logical views from the source system into OneLake so that source‑defined business logic—such as joins, filters, and transformations—can be preserved directly in Fabric. Together, CDF and Views enable incremental pipelines, near real‑time analytics, and shaped datasets that are immediately ready for consumption across Fabric workloads. Extended Capabilities are enabled during mirror setup and operate on top of core mirroring, allowing customers to selectively opt into advanced functionality as their analytics and AI needs grow. Billing will be available as part of these extended capabilities starting April 1, 2026. More details about these capabilities and billing can be found on our documentation: Extended Capabilities in Mirroring – Overview. Mirrored database now supports up to 1000 tables To meet growing business demands and improve scalability, mirrored databases now support up to 1000 tables, raised from the previous limit of 500. This enhancement significantly expands the scale of datasets that can be mirrored from the source database, enabling customers to bring more comprehensive data into Fabric without fragmentation, drive deeper analysis and scale the data solution to meet evolving requirements. Learn more in Mirroring in Microsoft Fabric. That’s a wrap! Publishing this update on the first day of FabCon feels especially meaningful. The features in this release reflect not just ongoing platform investment, but the ideas, feedback, and candid conversations we continue to have with the Fabric community—in sessions, online, and across preview programs. Thank you for showing up, sharing your experiences, and helping shape where Fabric goes next. We encourage you to explore these updates, ask questions, and tell us what’s working—whether that happens here at FabCon, in community forums, or through ongoing feedback channels. We’re grateful to be building Fabric alongside such an engaged community, and we’re excited to keep learning from you throughout FabCon and beyond.Fabric February 2026 Feature Summary
Welcome to the February 2026 Microsoft Fabric update! This month brings a wide range of enhancements across the Fabric platform—from improvements to the OneLake Catalog and developer experiences, to meaningful updates in Data Engineering, Data Factory, Real‑Time Intelligence, and more. Whether you’re building, operating, or scaling solutions in Fabric, there’s plenty here to explore. And with FabCon just weeks away, February’s updates are a great preview of what’s ahead. Don’t miss your chance to get Fabric certified for FREE If you are ready to take your Fabric exam in the next month, the Fabric team would like to give you a 100% voucher to cover the cost. Request a voucher by February 28, 2026. Terms and conditions apply. Three weeks until FabCon – will we see you there? Join us for the ultimate Power BI, Microsoft Fabric SQL, Real-Time Intelligence, AI, and Databases community-led event from March 16-20, 2026, in Atlanta, GA. The third annual FabCon Americas will feature sessions from your favorite Microsoft and community speakers, keynotes, more opportunities to Ask the Experts for 1:1 support, an engaging community lounge with opportunities to network and connect with your peers, a dedicated partner pre-day, a packed expo hall, attendee favorites Power Hour and the Data Viz World Championships live finals, and a can’t-miss attendee party at the Georgia Aquarium. Register with code FABCOMM to save $200. Contents Fabric Platform Workspace Apps now in the OneLake Catalog Streamlined item details Managing Fabric Identity limits within your tenant Horizontal Tab Display Settings Data Engineering Enhanced notebook version history with multiple sources Python notebooks add %run support Full size mode in Fabric notebook Announcing Private Link Support for Microsoft Fabric API for GraphQL CI/CD for API for GraphQL (Generally Available) Support for default arguments for Fabric user data functions Microsoft ODBC Driver for Microsoft Fabric Data Engineering (Preview) Customer Managed Key Encryption Support for Notebook Code Data Science Semantic Link 0.13.0 is Live Monitoring Real-Time Scoring Model Endpoints Data Warehouse Export migration summary SQL Pool Insights Real-Time Intelligence Effortless Real-Time Data Connection Streaming real-time data from private networks into RTI with Eventstream connectors Faster insights: real-time dashboard performance improvements Data Factory Recent data: Get back to your data faster (Preview) Improvements to the Fabric variable libraries integration in Dataflow Gen2 Relative references with Fabric connectors in Dataflow Gen2 Introducing Dataflow Gen2's just-in-time publishing mechanism Modern Evaluator for Dataflow Gen2 (Generally Available) Incremental copy from Fabric Lakehouse now supports both CDF and watermark-based methods in Copy job SAP Datasphere outbound for Amazon S3 and Google cloud storage in Copy job Column Mapping in CDC for Copy Job Rowversion now supported as an incremental column in SQL database Copy job Copy job activity now supports Service Principal and Workspace identity authentication Parallel Read Support for Large CSV Dataset Adaptive Performance Tuning: Intelligent Optimization for Data Movement (Preview) Other Fabric VS Code extension for browsing, editing item definitions, and MCP support February Monthly Update Video Fabric Platform Workspace Apps now in the OneLake Catalog Workspace Apps (Apps V2) are now supported in the OneLake Catalog, so you can discover, browse, and open apps directly from the Insights category. This category focuses on business-ready content designed to help you analyze, visualize, and report on data to drive actionable insights. With this update, Workspace Apps appear alongside other business-facing content such as organizational apps and reports, making it easier to explore all relevant insights in one place without needing to switch between different experiences. The Catalog also surfaces key metadata for each Workspace App, helping you quickly understand what the app contains before opening it. From there, you can open the app directly and start exploring insights right away. With the addition of Workspace Apps, the OneLake Catalog now includes all item types available in Microsoft Fabric, making it the central place to discover, understand, and access your Fabric content. Figure: Workspace Apps displayed in the OneLake Catalog under the Insights category. Learn more about the OneLake catalog in the OneLake catalog overview documentation. Streamlined item details The updated Item Details experience now extends beyond the OneLake Catalog to include full-page experiences for items accessed from outside the catalog. For example, when you open a Semantic Model directly from Workspaces, you will now see a modern, unified details page that matches the streamlined in-context experience found within the OneLake Catalog. This update brings a consistent design language across Fabric, offering improved usability and access to richer metadata: the enhanced details page now features the complete schema of all OneLake stored data items, making it easier to understand item characteristics. It also shows and visualizes item-level lineage, managing permissions and monitoring run/refresh history, all in one place. You can quickly find this key information, whether they’re navigating through the catalog or opening items in a standalone context. Figure: A Lakehouse in the new item details experience Managing Fabric Identity limits within your tenant Fabric Identity governance at scale just got easier. We are introducing a new tenant admin setting that gives you control over the maximum number of Fabric identities (hence Workspace identities) in your organization. With this update, Fabric tenant admins can: Scale beyond previous constraint—the default limit for number of Fabric identities in an organization increases from 1,000 to 10,000 identities. Set custom limits for how many Fabric identities can be created in their tenant. Manage limits programmatically by using the Update Tenant Setting REST API. How it works: The new setting “Define maximum number of Fabric identities in a tenant” is in the Fabric Admin portal in Tenant settings, within Developer settings. When the setting is disabled (the default), your tenant supports up to 10,000 Fabric identities—a 10x increase from the previous limit. Enable the setting to specify your own maximum. The value you enter becomes the upper limit for Fabric identity creation across your tenant. Note: Fabric doesn't validate that your custom limit falls within your Entra ID resource quota. Before setting a custom limit, check your organizations Entra ID service limits. If a workspace admin tries to create a new workspace identity that would exceed the limit, they'll see a clear error message explaining the reason. Figure: Configuring Maximum number of Fabric Identities in a tenant. You can also manage this setting programmatically using the Update Tenant Setting API. Sample HTTP request: POST https://api.fabric.microsoft.com/v1/admin/tenantsettings/ConfigureFabricIdentityTenantLimit/update { "enabled": true, "properties": [ { "name": "FabricIdentityTenantLimit", "value": "100", "type": "int" } ] } Sample JSON response: Status code: 200{ "tenantSettings": [ { "settingName": "ConfigureFabricIdentityTenantLimit", "title": "Define maximum number of Fabric identities in a tenant", "enabled": true, "canSpecifySecurityGroups": false, "tenantSettingGroup": "Developer settings", "properties": [ { "name": "FabricIdentityTenantLimit", "value": "100", "type": "Integer" } ] } ] } To learn more about identities in Fabric, see the documentation. For more information about all the tenant admin settings in Fabric, see the Tenant settings index. Horizontal Tab Display Settings To give developers more control over how they navigate open items in Microsoft Fabric, we’ve introduced new horizontal tab display settings. These settings let you tailor how tabs appear across the top of the Fabric interface—helping you stay organized and maintain focus during complex multitasking workflows. What’s new: Open the tab settings menu to quickly access tab display options by right‑clicking any tab. Figure: Open the horizontal tab display settings directly from a tab’s right-click menu. Two display modes Full tab names always show each tab’s full name for maximum clarity. Adaptive truncated names automatically shorten names when space is limited, allowing more tabs to remain visible. Figure: Horizontal tab display modes, including full tab names and adaptive truncated names, configured in Preferences page in Settings. Overflow menu When space runs out, tabs automatically collapse into a clean overflow list, making it easy to jump to any open item. Figure: Tabs automatically move into an overflow menu when there is insufficient space in the horizontal tab bar. These enhancements streamline navigation for developers working across multiple items and workspaces, reducing friction and improving overall multitasking efficiency. Find more details in this documentation. Data Engineering Enhanced notebook version history with multiple sources Keeping track of how a notebook evolves gets tricky when changes can come from different entry points—editing in the Fabric portal, syncing from source control, or other update flows. Fabric notebooks seamlessly integrate with Git, deployment pipelines, and Visual Studio Code. Each saved version is automatically captured in the notebook’s version history. Versions may originate from direct edits within the notebook, Git synchronizations, deployment pipeline, or publishing via VS Code. The source of each version is clearly labeled in version history to provide full traceability. Figure: Multiple sources records in notebook version history With the Enhanced Notebook Version History with Multiple Sources support, Fabric Notebooks now surface a clearer, more trustworthy history by reflecting versions from multiple origins, helping you trace changes, collaborate with confidence, and roll back to the right point when needed. Especially in CI/CD workflows (Git sync, deployment pipeline, public API), and team-authored workflows. Learn more about version history in the Version history documentation. Python notebooks add %run support Python developers often want to keep notebooks modular—shared utilities, setup logic, and reusable helpers shouldn’t be copy‑pasted everywhere. Python notebooks now support %run, enabling a familiar pattern for executing shared “code modules” and reusing logic across notebooks. This makes it easier to structure projects cleanly, iterate faster, and maintain common code in a single place. Figure: Reference another python notebook with intellisense Figure: Reference run python notebook You can use %run to reference and execute other notebooks within the same execution context, allowing you to directly call functions and reuse variables defined in those notebooks. Currently, %run in Python notebooks supports referencing notebook items only. Support for running code modules (such as .py files) from the notebook resources folder is coming soon—stay tuned. You can reference the reference run a notebook documentation for the detailed usage. Full size mode in Fabric notebook Full-size mode of cells is now available on Fabric notebook. When you’re working on a long or complex cell, the surrounding UI can get in the way. Full Size Mode lets you expand a single cell to fill the notebook for distraction‑free editing—ideal for deep refactors, large SQL or Python blocks, or screensharing. In full‑size mode, you retain full editing capability, stay focused on the selected cell, and can conveniently navigate to the previous or next cell without leaving the focused view. Figure: Enable full size mode on cell toolbar. Figure: Example of full-size mode. Learn more: Develop, execute, and manage notebooks Announcing Private Link Support for Microsoft Fabric API for GraphQL Microsoft Fabric API for GraphQL now supports Tenant Level Private Link, bringing enterprise-grade network security to your data APIs. This highly requested feature enables organizations to access their GraphQL APIs through private connectivity, ensuring data traffic never traverses the public internet. Secure data access with Private Link: This feature allows organizations to access GraphQL APIs through Microsoft’s private backbone network, improving security by preventing exposure to public internet threats and supporting compliance requirements. Simplified network management: Private Link reduces the need for complex firewall rules or VPN setups by allowing API calls only through approved private endpoints, easing governance and integration with existing Azure Private Link infrastructure. Enterprise-ready security model: Enabling Private Link at the tenant level integrates GraphQL APIs into a secured network environment complemented by Microsoft Entra ID authentication and flexible security options like single sign-on and saved credentials. To enable private link, update your tenant admin settings to enable Azure private link. Figure: Enable private link for your tenant Learn more Private link support for API for GraphQL. CI/CD for API for GraphQL (Generally Available) With this release, we have made improvements to reliability and performance on of the experience with Fabric CI/CD and deployment pipelines experience. Your teams can manage GraphQL artifacts in Git, collaborate with familiar pull-request workflows, and promote changes across environments using CI/CD—bringing the same engineering rigor to APIs that you already use for code and data. With CI/CD support, you can do the following: Git-enabled source control for your GraphQL API artifacts so you can version, review, and roll back changes. Support with Fabric deployment pipelines that allow you to build release pipelines for managing API for GraphQL items. Improved collaboration with pull requests, code reviews, and branching strategies applied to API changes. Figure: Screenshot of source control for API for GraphQL Learn more about API for GraphQL CI/CD and source control. Support for default arguments for Fabric user data functions Fabric User data functions now support default argument values, allowing omitted arguments to use preset defaults, which simplifies function calls and enhances code flexibility. This feature supports various input types including strings, boolean, floats, int, arrays, and objects. Functions become more versatile as they can handle common use cases with fewer arguments, while still allowing for customization when needed. Figure: Code snippet of using function with default arguments Learn more about default arguments for user data functions. Microsoft ODBC Driver for Microsoft Fabric Data Engineering (Preview) ODBC (Open Database Connectivity) is a widely adopted industry standard that enables applications to connect to and work with data across databases and big data platforms. Today, we’re introducing the Microsoft ODBC Driver for Microsoft Fabric Data Engineering (Preview) - an enterprise‑grade connector that delivers secure, reliable, and flexible Spark SQL connectivity for .NET, Python, and other ODBC‑compatible applications and BI tools, all powered through Microsoft Fabric’s Livy APIs. Built specifically for Fabric Data Engineering, this driver offers deep integration with OneLake and Lakehouse data, supports environment‑based execution, and enables flexible Spark configuration tailored to your workloads. With full ODBC 3.x compliance, Microsoft Entra ID authentication, comprehensive Spark SQL and data type support, performance optimizations for large datasets, and enterprise‑ready features like proxy support and session reuse, the Microsoft ODBC Driver helps teams accelerate Spark‑powered data engineering with the security, reliability, and performance expected in modern enterprise environments. Figure: The animated GIF demonstrates how to get started using ODBC driver To download and learn more about the Microsoft ODBC Driver for Microsoft Fabric Data Engineering, please refer to official documentation: Microsoft ODBC Driver for Microsoft Fabric Data Engineering. Customer Managed Key Encryption Support for Notebook Code Enterprise teams can now run Microsoft Fabric Notebooks in CMK‑enabled workspaces with Notebook content and metadata encrypted at rest using customer‑owned keys in Azure Key Vault, supporting stricter governance and compliance requirements without changing developer workflows. What’s new: Notebooks are fully supported in CMK‑enabled workspaces With this update, Notebooks can be created and used in workspaces where CMK encryption is enabled, and the Notebook content and associated Notebook metadata stored as part of Data Engineering items are protected using the workspace’s customer‑managed key. Concretely, this covers core Notebook content artifacts such as cell source, cell output, and cell attachments, so the key you control can be applied consistently to what developers author and what the system stores for notebook execution and collaboration. To enable CMK for your Fabric workspace (and use Notebooks in that CMK‑enabled workspace), follow the official documentation: Customer‑managed keys for Fabric workspaces Data Science Semantic Link 0.13.0 is Live With the 0.13.0 release, Semantic Link continues to expand its Fabric coverage and management capabilities. This update introduces new modules for lakehouse, reports, semantic models, SQL endpoints, and Spark, enabling end‑to‑end workspace operations—from creating and managing lakehouses and tables, to cloning and rebinding reports, refreshing and monitoring semantic models, and administering SQL and Spark settings. Several Fabric APIs are now surfaced consistently across modules, simplifying common workflows and improving API discoverability. The release also includes targeted API refinements and bug fixes, improving reliability for service principal authentication and correctness when evaluating measures. Overall, 0.13.0 makes it easier to manage Fabric assets programmatically at scale with stronger consistency and control. Explore the release notes. To help you explore these scenarios in practice, we’ve also published three short demos showcasing Sempy for data science, Sempy for Power BI automation, and Sempy for data engineering, illustrating how Semantic Link can unify workflows across personas and accelerate development within Fabric. Cast your vote for additional capabilities from Semantic Link Labs to be included in Semantic Link. Monitoring Real-Time Scoring Model Endpoints The new monitoring experience for real‑time scoring endpoints in Microsoft Fabric provides clear visibility into request volume, error rates, and latency as models run in production. Teams can easily compare these metrics across endpoint versions to validate improvements, catch regressions early, and make confident rollout or rollback decisions based on real usage. From tracking adoption to diagnosing issues and ensuring consistent performance underload, endpoint monitoring helps teams move faster from insight to action—delivering more reliable ML experiences while staying focused on scaling impact and business value. Data Warehouse Export migration summary Export Migration Summary is a new capability in Migration Assistant that makes it simple, reliable, and secure to download your full migration results in formats that best fit your workflow. The export option is available directly from the Migration Assistant’s summary view and full screen view. Figure 1: Export menu Figure 2: Export file formats Once triggered, the export runs reliably in the background, even if the Migration Assistant window is closed, ensuring a smooth workflow for large, multi-object migrations. The following output formats are supported: Excel Fully structured workbook with two worksheets: Migrated Objects and Objects To Fix MIP-compliant and aligned with your organization’s sensitivity labels. CSV Lightweight and tool-friendly Each exported file provides a structured, comprehensive view of your migration results, including: Field Description Object name Name of the SQL object Object type SQL object types such as table, view, function, stored procedure. State Translation State Adjusted: Fabric Data Warehouse compatible updates are applied Not adjusted: No change in the original script Details List of adjustments applied or error messages Type of error Type of error as Translation message, Translation error, Translation apply error Figure 3: Fields in exported file This structure enables teams to aggregate migration details at object or object type level and identify patterns across objects. Export Migration Summary removes a major blocker for customers who need shareable, reliable artifacts that reflect the true state of their migration progress. Learn more in the Migration Assistant for Fabric Data Warehouse documentation. SQL Pool Insights Understanding why workloads slow down often require visibility beyond individual queries. SQL Pool Insights extends the existing Query Insights experience with pool‑level telemetry, helping you understand how resources are allocated and when pools are under pressure in Microsoft Fabric Data Warehouse. Figure - The image shows the sql_pool_insights schema With SQL Pool Insights, you can: Monitor the health of built‑in SELECT and NON SELECT SQL pools. Track pressure events, configuration changes, and capacity updates over time. Correlate pool‑level pressure with query performance using existing Query Insights views. Validate resource isolation between read‑optimized and write‑optimized workloads. This feature adds a new system view — queryinsights.sql_pool_insights — that logs pool state changes and sustained pressure events, giving you actionable signals for troubleshooting performance issues and planning capacity more effectively. Learn more about SQL Pool Insights Real-Time Intelligence Effortless Real-Time Data Connection Connecting data is often the first step on a user’s Real-Time Intelligence journey—and it should be effortless. Previously, the left navigation in the Real-Time hub (RTH) included two separate entries for connecting data—Data sources and Azure sources. While well intentioned, this distinction didn’t always match how users think about the task at hand. The most common question wasn’t about categories; it was just how to add data. To better reflect that reality, we’ve unified these entry points into a single menu item: Add data. With this update: There’s now one clear place to begin when connecting data. The navigation focuses on intent, not source taxonomy. Users can move faster without second‑guessing their choices. Under the hood, nothing has changed. You still have access to the same rich set of data sources, including all out-of-box data connectors, Azure sources, Azure Diagnostics logs, and more. What’s changed is the experience—clearer, simpler, and designed to help you get value faster. Note: we are rolling this change out gradually, so you may see it in the coming weeks. Figure: Real-Time hub left navigation before the change Figure: Real-Time hub left-navigation after the change Figure: New data connector context menu Learn more about Real-Time Hub. Try it out and share your feedback. Streaming real-time data from private networks into RTI with Eventstream connectors Real-Time Intelligence Eventstream is designed to bring real-time data from diverse sources, transform it, and effortlessly route it to various destinations. For sources that run in private network environments , such as cloud virtual network or on-premises infrastructures, a secure method is required to allow Eventstream to access the source. The streaming connector’s support for virtual networks (vNet) and on-premises environments offers a secure, managed pathway, enabling Eventstream to reliably connect with these private-network streaming sources. To enable data transfer from a source within a private network into Eventstream, it is necessary to establish an Azure managed virtual network as an intermediary bridge, as illustrated in the diagram. The Azure virtual network should be connected to the private network hosting the data source using appropriate methods, such as VPN or ExpressRoute for on-premises scenarios, and private endpoints or network peering for Azure sources, etc. Subsequently, the Eventstream streaming connector instance will be injected into this virtual network through SWIFT injection, allowing secure connectivity between the connector and the data source located within the private network. Figure: Eventstream connectors private network support overview To facilitate streaming connector vNet injection into an Azure virtual network you’ve created, Fabric provides a centralized location for network or data engineers to manage the references to Azure virtual network resources. The streaming virtual network data gateway in Fabric serves this purpose for Eventstream. Unlike 'Virtual network data gateways' and 'On-premises data gateways' , this new option does not require cluster provisioning or additional capacity. However, the user experience across all three gateway types remains largely similar. Create and manage the ‘streaming virtual network data gateway’ in the ‘Manage Connections and Gateways’ page in Fabric. Select it when setting up streaming connections for Eventstream sources using the Get Events wizard or 'Streaming virtual network'. After that, you can configure your Eventstream data source as usual. Figure: Streaming virtual network data gateway configuration for Eventstream To help familiarize yourself with the end-to- end flow of the feature check out this detailed demo on Streaming Real-Time Data from Private Networks into RTI with Evenstream Connectors. For a step-by-step guide on getting started, please refer to the document: Connect to Streaming Sources in Virtual Network or On Premises with Eventstream. Faster insights: real-time dashboard performance improvements Based directly on community feedback, we’ve optimized the Real-Time Dashboard from the ground up, ensuring a snappier, high-performance experience. Thanks to a series of performance optimizations across the dashboard experience, we’ve achieved a significant double-digit reduction in full dashboard load time, along with major improvements in common interactions:‑digit reduction in full dashboard load time, along with major improvements in common interactions: Much faster initial dashboard load—in some scenarios, up to 6× faster Large dataset visualizations load dramatically quicker, reducing wait time and friction Charts render more efficiently (including up to 10× faster pie charts) Smoother, more responsive UI, with freezes and visual jumps eliminated Whether you are loading large datasets or refreshing live visuals, the UI is now smoother and significantly more responsive, ensuring your data keeps pace with your decisions. Note: The video demonstrates performance benchmarks conducted in a controlled internal environment. Learn more about What is Real-Time Dashboard? Data Factory Recent data: Get back to your data faster (Preview) When you work with the same data sources repeatedly in Dataflow Gen2, the new Recent data (Preview) module helps you access your most frequently used data faster. Why this matters Provides quick access to frequently used tables, files, folders, databases, sheets, etc. Eliminates repetitive navigation steps Improves productivity in transforming data by efficiently connecting and ingesting data How to access Recent data Getting started is straightforward. Open any Dataflow Gen2 in your Fabric workspace and you'll find two convenient ways to access Recent data. First, you can select Recent data directly from the Power Query ribbon for immediate access to your history. Figure: Recent data in the Power Query Ribbon Alternatively, select Get data and choose the Recent data module from the home tab or dedicated tab. Figure: Recent data in Modern Get Data When you select an item from your Recent data module, it loads directly into the Power Query editor without additional navigation steps required by default. You can start applying transformations immediately. If you need to explore related items in the same location, select Browse location to discover other tables or files in the same folder or database, making it easy to include additional related data in your dataflow. This Preview feature is available now, learn more in the Recent data documentation. Improvements to the Fabric variable libraries integration in Dataflow Gen2 In September 2025, we released a preview of the Fabric variable libraries integration with Dataflow Gen2. This update address two of the most common feedback themes: Variable limit: Dataflows no longer have a limit on how many variables it can retrieve per evaluation. Power Query editor support: the data preview shown in the Power Query editor evaluates the variables. This includes both the usage of the Variable.Value and Variable.ValueOrDefault functions. Figure: Screenshot of the Power Query editor for Dataflow Gen2 rendering the output of the Variable.Value function for a Variable Library with the name My Library and a variable with the name MyDateTime We also identified and fixed issues that caused saving issues when variables were used in data destinations, and we improved the overall Power Query editor experience when variables are used in navigation steps. We’re continuing to improve our experience and will share updates in the coming months. Be sure to leave your feedback in the Data Factory community forum where you can engage directly with us if you have any questions or suggestions. Learn more from the Use Fabric variable libraries in Dataflow Gen2 (Preview) documentation. Relative references with Fabric connectors in Dataflow Gen2 One of the core principles of Fabric and Data Factory is enabling solutions that are CI/CD-ready. In Dataflow Gen2, you can already use public parameters and Fabric variable libraries to make your solutions dynamic and compatible across deployment pipelines. We’re introducing a new capability to simplify CI/CD scenarios when using Fabric connectors: Relative References. What’s changing? Previously, when you used Fabric connectors (Lakehouse, Warehouse, or SQL Database), the generated script relied on absolute references—such as Workspace ID and item IDs (e.g., Lakehouse ID, Warehouse ID). With Relative References, you’ll see a new node in the navigation dialog called (Current Workspace). This allows you to select items within the current workspace context. Once selected, the script will reference the item name instead of unique IDs. Figure: Diagram comparing the possible experiences between absolute and relative references and the M script crated for each. Why it matters This approach ensures that when you move your solution from development to testing or production, no script changes are required. Your Dataflow will continue to work based on item names, making deployments seamless without adding any extra components Learn more Check out the documentation for Fabric Lakehouse, Fabric Warehouse, and Fabric SQL connectors for more information. Introducing Dataflow Gen2's just-in-time publishing mechanism Previously, Dataflow Gen2 required you to manually trigger a publishing operation before running or refreshing a dataflow whenever unpublished changes were present. With the updated experience, the run/refresh operation now automatically checks if a publication is needed and completes it as part of the job. This simplifies the workflow and ensures that runs succeed without requiring an explicit publish step. You can still rely on the following behaviors: Explicit publish control: You can continue to trigger a publish directly using the Publish job when you need full control. Saving in the UI: Saving a dataflow in the authoring UI still performs a publication as part of the save process. Longer first refresh: The first refresh after making changes may take longer, because publishing now happens automatically as part of that initial run. CI/CD deployments: When deploying across environments, a separate publishing step is no longer required. The first run in the target environment will be published automatically if needed. Learn more about this new mechanism from: Dataflow Gen2 with CI/CD and Git integration. Modern Evaluator for Dataflow Gen2 (Generally Available) The Modern Query Evaluation Engine (Modern Evaluator) for Dataflow Gen2 brings substantial performance and reliability improvements to data transformation workloads across Microsoft Fabric. Built on .NET 8, this engine delivers faster execution, more efficient processing, and improved scalability for complex dataflows. As part of its GA rollout, the Modern Evaluator now supports more than 80 connectors, significantly expanding coverage across enterprise and SaaS data sources. Key improvements in this release Broad connector support: the Modern Evaluator now works with 80+ connectors—including Azure Data Explorer, Lakehouse, Warehouse, Salesforce, Google Analytics, Fabric-native sources, and many more. This includes some SQL-based connectors such as Fabric SQL Database, SQL Server Database and others. This expanded coverage ensures that most Dataflow Gen2 scenarios can benefit from the improved engine. Faster and more efficient Web requests: Enhancements to Web connector handling result in lower overhead for HTTP-based data sources. Customers can expect smoother query execution and improved resilience when working with REST APIs or other web endpoints-based data sources. Customers can expect smoother query execution and improved resilience when working with REST APIs or other web endpoints. ‑based data sources. Customers can expect smoother query execution and improved resilience when working with REST APIs or other web endpoints. Learn more about the modern query evaluator in Dataflow Gen2 and its compatible connectors: Modern Evaluator for Dataflow Gen2 with CI/CD. Incremental copy from Fabric Lakehouse now supports both CDF and watermark-based methods in Copy job When performing incremental copy from a Fabric Lakehouse table, we strongly recommend using CDF (Delta Change Data Feed) to capture row inserts, updates, and deletions, and replicate them to supported destinations. Figure: Incremental copy from Fabric Lakehouse via CDF. However, you can now also optionally use watermark-based incremental copy without enabling CDF. In this mode, you can select an incremental column for each table to identify changes. To enable this, go to the Advanced Settings button after creating the Copy job, where you will have the option to switch from CDF to using a watermark column. Figure: Incremental copy from Fabric Lakehouse via watermark-based method Learn more from the What is Copy job in Data Factory documentation. SAP Datasphere outbound for Amazon S3 and Google cloud storage in Copy job Previously, you could use SAP Datasphere Outbound for ADLS Gen2 in Copy job to perform CDC replication from SAP to any supported destination. For more details, see Tutorial: Copy job with SAP Datasphere Outbound (Preview). You can now also use SAP Datasphere Outbound for Amazon S3 and SAP Datasphere Outbound for Google Cloud Storage, expanding staging storage support across multiple clouds so you can choose the option that best fits your scenario. Figure: Selecting Copy data from SAP Datasphere Outbound for Amazon S3 and Google Cloud Storage Learn more from Tutorial: Copy job with SAP Datasphere Outbound (Preview) - Microsoft Fabric | Microsoft Learn Column Mapping in CDC for Copy Job Column mapping from source to destination is now supported during CDC replication within the Copy job. This is useful when you want to rename columns, change data types, or otherwise customize the schema in the destination store. Column mapping now is supported across all data movement patterns in Copy job, including full copy, watermark-based incremental copy and CDC replication. Figure: Column Mapping in Copy job. Learn more from Change data capture (CDC) in Copy Job - Microsoft Fabric | Microsoft Learn. Rowversion now supported as an incremental column in SQL database Copy job Copy job simplifies data movement from many sources to many destinations by natively supporting multiple delivery styles, including bulk copy, incremental copy, and change data capture (CDC) replication. For incremental copy, the first run performs a full copy, and subsequent runs transfer only new or changed data from the last run to save time and resources. If CDC is not enabled on your database, you must select an incremental column for each table. This column acts as a marker, allowing Copy job to identify rows that are new or updated since the last run. Previously, this column was limited to date/time values or increasing numeric values. You can now also select RowVersion as the incremental column when performing incremental copy from SQL Server, Azure SQL Database, SQL Managed Instance, or SQL in Fabric. Figure: Selecting RowVersion to identify changes when incremental copy from SQL database Learn more from What is Copy job in Data Factory - Microsoft Fabric | Microsoft Learn. Copy job activity now supports Service Principal and Workspace identity authentication We are expanding authentication support in the Copy job activity in pipeline, making it easier than ever to securely connect, integrate, and move data across a broad range of enterprise and SaaS systems. This enhancement reflects our continued commitment to delivering an enterprise-ready data integration platform that balances security, flexibility, and ease of use. The Copy job activity now supports additional authentication methods to your Copy job item in pipeline, enabling customers to choose the security model that best fits their organizational standards and compliance requirements. The added authentication types are service principal and workspace identity. Figure: Authentication kind in Copy job activity. Why this matters Authentication is no longer just a connection detail; it is a foundational requirement for enterprise-scale data integration. With expanded authentication support in the Copy job activity, customers can now: Strengthen security posture by minimizing long-lived secrets and adopting identity-based access. Accelerate time to value by connecting to copy job items using native, first-class authentication mechanisms. Simplify compliance and audits through standardized authentication patterns aligned with enterprise security policies. Improve operational reliability by leveraging managed identity and token-based access. These improvements are especially impactful for organizations operating in regulated industries or managing large-scale hybrid environments where security consistency is non-negotiable. Getting started The new authentication options are available within the Copy job activity setting in Fabric Data Factory. Explore these new capabilities and start standardizing modern, secure authentication patterns across your data integration workflows. Learn more about the authentication capability in copy job activity. Parallel Read Support for Large CSV Dataset As we continue improving ingestion performance, we’ve introduced a new enhancement for reading CSV datasets in Data Factory. It significantly boosts ingestion throughput for large CSV files—a common customer challenge when a single file can’t fully take advantage of parallel reads. With this update, Data Factory can now read large CSV files in parallel when the format allows for safe partitioning, delivering better performance and scalability while preserving correctness. By using your multiline configuration, the service can determine how to split the file and process it in parallel, dramatically improving read performance. When multiline behavior is explicitly defined, the service can: Safely identify record boundaries even in large files. Partition the file into multiple logical chunks. Read and process those chunks concurrently. Figure: Multiline rows setting for reading delimited text. This enables higher throughput without compromising data correctness. Why multiline configuration matters CSV files that contain multiline records such as fields with embedded line breaks enclosed in quotes which require special handling. Without explicit configuration, the system must assume the most conservative parsing model, which prevents parallelization. By specifying multiline information on the source, users provide the necessary context for the service to: Correctly interpret row boundaries. Avoid recording corruption during parallel reads. Confidently enable parallelism where it is safe. This opt-in design ensures that performance improvements are applied only when they are valid for the data format. Getting started We encourage customers working with large CSV datasets to review the source configurations and unlock the benefits of this new capability to take advantage of parallel reads for large CSV files. Learn more about the performance optimization for copying delimited text files. Adaptive Performance Tuning: Intelligent Optimization for Data Movement (Preview) Adaptive Performance Tuning is designed to intelligently optimize data movement performance based on your configuration and runtime context. This feature represents a major step forward in making performance tuning simpler, safer, and more effective without requiring deep manual expertise or trial-and-error adjustments. As data volumes grow and integration scenarios become more diverse, achieving optimal performance has become increasingly complex. Customers must balance throughput, reliability, cost, and data correctness across a wide range of sources, destinations, formats, and network environments. Adaptive Performance Tuning addresses this challenge by allowing the service to dynamically apply performance optimizations informed by customer configurations and real execution conditions. Adaptive Performance Tuning is designed with safety and predictability as first principles. Optimizations are applied only when they are compatible with the configured semantics of the task, ensuring that performance gains do not compromise data accuracy or expected behavior. As a preview feature, Adaptive Performance Tuning is: Explicitly opt-in, giving customers full control. Non-breaking, with no required changes to existing configurations. Incrementally evolving, informed by customer feedback and real-world use. Adaptive Performance Tuning is part of a broader vision to make Data Factory a more intelligent, self-optimizing platform. By combining rich configuration signals with service-side intelligence, we aim to help customers focus less on infrastructure tuning and more on delivering business value from their data. Figure: Adaptive performance tuning setting. As the preview evolves, we plan to expand the range of supported optimization scenarios, continuously improving performance outcomes. Getting started with the preview Customers can enable Adaptive Performance Tuning directly within their pipeline settings and begin benefiting from service-driven performance optimization. We encourage users to try the preview, monitor performance improvements, and share feedback to help shape the future of this capability. Learn more about the Adaptive Performance Tuning feature. Other Fabric VS Code extension for browsing, editing item definitions, and MCP support The Microsoft Fabric extension for Visual Studio Code has been enhanced to improve the user experience in exploring, editing, and managing Fabric items directly within the editor and through integration with Fabric MCP server and GitHub Copilot chat. Browse workspace folders: Users can now view and drill into folders and their contents within the workspace to better understand the organization of Fabric content without leaving VS Code. View and edit Fabric item definitions: The extension supports viewing item definitions in read-only mode by default, but you can enable editing through extension’s settings. Changes saved directly update the Fabric item in the workspace, but users should proceed cautiously to avoid breaking changes. Fabric MCP server integration: The Fabric MCP server extension can be enabled alongside the Fabric and GitHub Copilot Chat extensions, offering tailored tools for working with Fabric artifacts, including CRUD operations, generating design documents, and accessing Microsoft Fabric documentation through a specialized agent mode. Figure: Using Fabric MCP in VS Code to design a data analytics solution. Learn more about the new features enabled for Fabric extension for VS code. That’s a wrap for February! We hope these updates help you work faster, build with confidence, and get even more value from Microsoft Fabric. With FabCon right around the corner, it’s an exciting time to connect with the community, learn from experts, and see these capabilities come to life. If you’re heading to Atlanta, we can’t wait to see you there—and if not, there’s still plenty to dig into until next month’s update.The future of conversational analytics in Fabric
What if you could ask questions about your business data in plain language, and get trusted answers wherever you work? Discover how Fabric IQ, Power BI, Microsoft 365 Copilot, and Fabric data agents are bringing the full breadth of conversational analytics into the flow of work, turning governed business context into insights and action.Fabric June 2026 Feature Summary
This month’s release brings a wide mix of improvements across the platform—from core data engineering and warehouse capabilities to continued investments in data agents, real-time intelligence, and enterprise-ready governance. You’ll see a strong focus on making complex scenarios more manageable, whether that’s simplifying storage and lifecycle management in OneLake, improving observability and reliability for data agents, or enabling more scalable, secure integrations across your data estate.
