Fabric Data Warehouse best practices for medallion architectures
Part three of a series on medallion architecture with Fabric Data Warehouse. Good medallion architecture is mostly operational discipline. In part one of this series, we chose the pattern, and in part two, we filled in the Bronze, Silver, and Gold layers. Now comes the part that usually determines whether the architecture holds up in production: the operating rules. Many medallion architectures look great on paper but become difficult to maintain as data volumes, business requirements, and consumers grow. A medallion pipeline is easy to explain and easy to demo. It is more challenging to keep clean over time. The challenges usually start small: row-by-row loads, report-specific logic in the wrong place, transformations that cannot be safely rerun, or Gold tables that slowly become another staging layer. Why this matters Most medallion problems are not caused by the names Bronze, Silver, and Gold. They happen because the pipeline stops behaving like a pipeline. Bronze starts cleaning. Silver starts serving dashboards. Gold starts compensating for upstream data quality. Before long, nobody knows where a rule belongs, and every change feels risky. The goal of best practices is not to add ceremony. The goal is to make the pipeline predictable: predictable loads, repeatable transformations, trusted outputs, and clear places to look when something breaks. Best practice 1: Batch the writes Fabric DW is built for set-based work. Treat ingestion and transformations as batches, not as a stream of tiny row-by-row operations. In Bronze, that usually means using COPY INTO, Fabric Pipelines, or other bulk-loading patterns to land data in raw tables. If you are loading from files, aim for fewer well-sized files instead of many tiny ones. When practical, files in the 100 MB to 1 GB range are a healthier starting point than a long tail of small files. In Silver and Gold, the same idea applies: prefer set-based T-SQL transformations, CTAS, INSERT...SELECT, and MERGE patterns over procedural row-at-a-time logic. Do this well Load Bronze in batches, and avoid trickle inserts when the source can be staged first. Add ingestion metadata, such as source file name and load timestamp, so every batch is traceable. Keep operational logging lightweight. If you need very high-write audit events, do not turn the warehouse into a single-row logging engine. Let Bronze preserve the batch; let Silver decide what is valid. Rule of thumb: if a load pattern creates a large number of tiny writes, fix the load pattern before tuning the query. Best practice 2: Make Silver rerunnable Silver is where the pipeline earns trust. That means Silver transformations need to be repeatable, testable, and safe to rerun. If an upstream source reloads, or a cleansing rule changes, you should know how to rebuild the affected Silver tables without guessing which reports need to be patched. This is where idempotent design matters: a transformation should produce the same result when run again against the same inputs. In Fabric DW, use CTAS when you want to materialize a clean table from a query, INSERT...SELECT for controlled incremental loads, and MERGE when late-arriving or changed data needs to update existing Silver rows. Do this well Design transformations so they can run again without duplicating or corrupting data. Use staging tables when the logic is complex. A few clear steps are easier to operate than one unreadable query. Put quality gates in Silver: required fields, valid formats, duplicate handling, and reason codes for rejected records. Choose precise data types and lengths. Silver is the right place to turn loose source data into reliable analytical data. Rule of thumb: if a report needs to clean the data again, Silver did not finish its job. Best practice 3: Shape Gold for consumption Gold is not just “the final table.” Gold is the business-facing serving layer. It should be modeled around how people ask questions, not around how the source systems store data. For some workloads, that means a star schema with fact and dimension tables. For others, it means a data mart, a wide reporting table, or a pre-aggregated summary. The pattern matters less than the principle: Gold should make the common analytical path simple, fast, and trustworthy. This is also where you should be careful not to let Gold become a junk drawer. If Gold is full of one-off fixes, report-specific exceptions, and raw technical fields, the layer is doing too much. Do this well Model the grain explicitly. A fact table without a clear grain becomes hard to explain and harder to debug. Pre-aggregate where the business repeatedly asks the same question. Hide technical fields that helped the pipeline but do not help the consumer. Keep Gold dependent on Silver by default. Direct Gold-to-Bronze dependencies should be rare and deliberate. Rule of thumb: Gold should answer the business question quickly without making the report author rediscover the pipeline. Best practice 4: Use Fabric DW defaults, but do not fight the engine Fabric DW gives you a SQL warehouse over Delta data in OneLake. That means you get transactional behavior, optimized storage patterns, and a managed engine that handles many physical decisions for you. The practical advice: do not bring every habit from traditional data warehousing with you. You do not need to micromanage distribution or indexing the same way you would in older platforms. Focus first on healthy data layout, set-based transformations, good table design, and predictable query patterns. At the same time, do not ignore the basics. Query performance still benefits from clean data types, useful statistics, well-shaped Gold tables, and avoiding unnecessary scans. Do this well Keep V-Order and platform optimizations on unless you have a measured reason to change them. Use the performance guidance for Fabric Data Warehouse before inventing custom tuning patterns. Check query behavior when a Gold table becomes critical to many reports. Treat advanced exceptions as exceptions. Most teams should start with the defaults and tune only when evidence says to tune. Rule of thumb: tune from evidence, not from habit. Best practice 5: Monitor by layer A medallion pipeline should be observable at each layer. If a dashboard is wrong or slow, you should be able to tell whether the issue started in Bronze ingestion, Silver transformation, or Gold serving. In Fabric DW, use Query Insights and the warehouse monitoring views to understand query behavior, expensive operations, and refresh patterns. Pair that with pipeline-level monitoring so you can see not only whether a job failed, but where the failure happened. Measure the pipeline in terms the team can act on: Bronze load duration, Silver transformation duration, rejected-record counts, Gold refresh duration, and Gold query performance. Do this well Track load and refresh duration by layer. Log the number of records received, accepted, rejected, and published. Watch critical Gold queries after refresh, especially the ones that feed executive dashboards or widely used semantic models. Keep operational alerts tied to business impact. A failed Gold refresh matters differently from a delayed Bronze load. Rule of thumb: if you cannot tell which layer failed, your monitoring is not layer-aware enough. Before moving a medallion pipeline into production, use the following checklist to verify that each layer is operating as intended. The best-practice checklist Area Bronze Silver Gold Write pattern Batch ingest with metadata Set-based transformations Scheduled refreshes Quality rule Preserve what arrived Validate, conform, and flag Expose trusted fields only Performance focus Avoid tiny writes Keep logic rerunnable Shape for common queries Takeaway Part two of this series was about one job per layer. Part three is about operating each job like it matters. Batch the writes. Make Silver rerunnable. Shape Gold for consumption. Use Fabric DW’s managed engine instead of fighting it. Monitor the pipeline by layer so failures are easy to locate and fixes happen in the right place. If you follow those rules, your medallion architecture becomes less fragile over time, not more. This post is part of our Medallion Architecture on Fabric Data Warehouse series: Choosing your medallion pattern in Fabric Data Warehouse Building the Bronze → Silver → Gold layers Fabric DW best practices for medallion architectures Securing and governing your layers Performance tuning your medallion pipeline Ready to go deeper? Explore the Microsoft Fabric Data Warehouse performance guidelines and ingestion guidance, then stay tuned for Part four of this series, where we’ll cover securing and governing your layers.Choosing your medallion pattern in Fabric Data Warehouse
Coauthor: Artur Vieira Part one of a series on medallion architecture with Fabric Data Warehouse. Medallion architecture is one of the most common patterns for organizing data in Microsoft Fabric, but successful implementations require a series of design decisions — from choosing the right architecture pattern to securing, governing, and optimizing your workloads. In this five-part series, we'll walk through the practical choices that shape a modern medallion implementation in Fabric Data Warehouse (DW), sharing recommendations, tradeoffs, and real-world guidance along the way. In this installment, we'll focus on the first and most important decision: choosing the right medallion pattern for your workload. Why this matters Most teams designing a medallion architecture in Microsoft Fabric start with the wrong question: "Should I use a Lakehouse or a Warehouse?" The better question is, "How much Spark do I actually need?" Your answer will shape everything from development workflows to security and long-term maintenance. Quick level-set: medallion organizes data into three layers — Bronze (raw), Silver (enriched), and Gold (curated) — to progressively improve data quality and structure. That’s the whole definition you need. The interesting part is how you map those layers onto Fabric. The key decision: Data Warehouse, Lakehouse, or both? Fabric gives you two major analytics storage options on OneLake: Lakehouse and Data Warehouse. Because Fabric DW is an enterprise-scale SQL warehouse built on the open Delta Lake format in OneLake — combining a relational SQL engine with lake storage, so data is stored as Delta Parquet with ACID transactions and time travel — you don’t have to choose between “warehouse” and “lake.” You’re really choosing how much of the pipeline you run in T-SQL versus Spark. That leads to two patterns worth recommending: an all-in-one or hybrid approach. Pattern A: All-in-One Data Warehouse Use Fabric DW for Bronze, Silver, and Gold, separated by schemas (for example Bronze.*, Silver.*, Gold.*) or by separate warehouses, with data flowing raw → curated entirely within the warehouse using T-SQL or Data Factory pipelines. Best when: most of your data is structured (or can be structured on load) and your team prefers SQL-centric development. Why it’s nice: one engine, one skill set, transactions and views for every hop. Pattern B: Lakehouse + Warehouse Hybrid Land raw data in a Lakehouse for Bronze (and optionally Silver), where you can use Spark for complex prep, then implement Gold in the Warehouse as the SQL serving layer for BI. This works seamlessly because OneLake underpins both — Silver or Gold data can be materialized to Delta tables and queried by the Warehouse’s SQL endpoint without copying. Best when: You have unstructured or semi-structured data, or need heavy data engineering in Python or Scala. Why it’s nice: The right engine for each layer — Spark for heavy prep, T-SQL for serving — with no data copying, since OneLake underpins both Which should you pick? Here’s the rule I’d give a customer: Rule of thumb: For most SQL-focused analytics teams, an all-in-one Fabric Data Warehouse is the pattern I’d recommend starting with. If you have a mix of unstructured data or need heavy data engineering, land and refine raw data in a Lakehouse, then serve the final Gold layer from the Warehouse. Fabric’s architecture lets you evolve, so you’re not locked in: start all-in-warehouse and add a Lakehouse later when a new source needs Spark, or vice versa. All-in-One DW Lakehouse + DW Hybrid Primary skill T-SQL Spark / Python + T-SQL Best for data Structured / relational Unstructured, semi-structured, streaming Bronze lives in Warehouse staging tables Lakehouse (raw files, delta tables or both) Gold lives in Warehouse Warehouse Pick it for Simplicity, one engine Flexibility, heavy engineering One layout rule, whichever pattern you choose Keep layer separation clear. Microsoft recommends isolating layers into different workspaces, or at least different Fabric items, for better control and governance. The payoff is real: separate items give you finer security control — only data engineers touch Bronze while analysts see only Gold — and clearer isolation, so an accidental change in Gold can’t affect Bronze. Takeaway Don’t frame this as “Warehouse vs. Lakehouse.” Frame it as “How much Spark do I need?” If your workloads are structured and SQL-first, choose an all-in-one DW. If they’re unstructured or engineering-heavy, choose a hybrid approach, with Gold always served from the Warehouse. As your implementation grows, consider separating Bronze, Silver, and Gold into distinct Fabric items or workspaces to simplify governance and security. Ready to go deeper? Explore the Microsoft Fabric documentation for Data Warehouse and OneLake, then stay tuned for Part 2 of this series, where we'll walk through howBronze, Silver, and Gold layers are implemented in practice. This post is part of our Medallion Architecture on Fabric Data Warehouse series: Choosing your medallion pattern in Fabric Data Warehouse Building the Bronze → Silver → Gold layers Fabric DW best practices for medallion architectures Securing and governing your layers Performance tuning your medallion pipeline In the next post, we'll explore what Bronze, Silver, and Gold layers actually look like in Fabric Data Warehouse and how data moves between them.Fabric August 2026 Feature Summary
Welcome to the August 2026 Fabric update! Microsoft Fabric continues to evolve with new capabilities that help organizations build, manage, and scale their data and AI solutions more efficiently. This month's updates introduce enhancements across Fabric Platform, OneLake, Data Engineering, Conversational Analytics, Data Warehouse, Real-Time Intelligence, and Data Factory. Whether you're strengthening governance, improving performance, streamlining development workflows, or expanding AI-powered experiences, these updates are designed to help you get more value from your data while simplifying day-to-day operations. Explore the following highlights to see what's new in Microsoft Fabric this month. Events and Announcements Join us for FABCON and SQLCON in Barcelona, September 28 – October 1, 2026 Explore what’s possible with Microsoft Fabric and get up to speed on the latest in SQL, analytics, and AI. From 130 sessions and 4 keynotes to workshops, the expo, community spaces, and the Power BI DataViz World Championships, this is where the data community comes together. Learn directly from Microsoft and community experts shaping the future of Fabric and SQL. Register now and save €200 with code FABCMTY200. Fabric Platform KQL-Dashboard Embed in Fabric (Preview) A new capability that lets you add interactive KQL-Dashboard content directly into your own browser-based web applications is available. Now, you can bring Fabric analytics into the apps, portals, and workflows your users already use. Analytics are most useful when they are available where decisions happen. With Fabric Embed, you can place Fabric content inside a custom application or internal portal instead of requiring users to switch to the Fabric portal. Fabric Embed can help you: Bring interactive analytics into an existing business workflow. Explore Fabric analytics without switching between your application and the Fabric portal. Keep Fabric workspace permissions and Microsoft Entra ID identities at the center of access control. Build user-based embedded experiences for people who already have access to the underlying Fabric item. The embedded experience complements the Fabric portal. Content owners can continue to create and manage analytics in Fabric while application developers present that content in the context most useful to their users. To learn more, refer to the Microsoft Fabric Embed documentation. Git Integration – Workspace Relation API (Preview) Following our recent announcement of branch workspaces and the relationship that’s automatically created when a user performs a branch-out operation, we’re introducing the new Workspace Relations API. These endpoints let you create, query, update, and delete relationships between Fabric workspaces programmatically — and for anyone building automation around Git integration, branched workspaces, and CI/CD, it’s a big deal! The workflow is straightforward. A developer creates a feature branch. An ADO pipeline or GitHub Action then provisions a new feature workspace, configures Git, applies the right settings and permissions, and synchronizes the workspace. Finally, the Workspace Relations API links that feature workspace back to its parent — closing the loop and giving your automation a first-class, queryable connection between parent and branched workspaces. Figure: Branch workspace relation. To learn more, refer to Development process using Branch-Out experience. Git Integration and Deployment Pipeline – Item level permission restriction Starting December 1, 2026, users without read-write permissions on workspace items can't use Git integration and won’t be able to deploy a workspace or assign workspace to a stage via Deployment Pipeline. This restriction can result in loss of access to certain items because of sensitivity labels and protection policies applied to those items. To learn more, refer to Information Protection in Microsoft Fabric. OneLake Resource instance rules for OneLake (Generally Available) Resource instance rules for OneLake are ready for production workloads across enterprise analytics environments. They give workspace admins a precise way to allow access from trusted Azure resource instances while continuing to enforce network and data-level protections. Use resource instance rules when you need to enable secure service-to-service access to OneLake without relying solely on IP allowlists or requiring private networking for every integration. Combined with Private Link, IP firewall rules, and identity-based permissions, they help organizations apply layered security based on the needs of each workspace and workload. Resource instance rules support a broad set of Azure services that can present a verifiable Azure resource identity, including Azure Databricks, Azure SQL Server, Azure Data Factory, Azure Event Grid, Azure Machine Learning, and more. Configuration requires only the Azure resource ID, simplifying setup while maintaining control over which Azure resources can access OneLake. To learn more, refer to Manage inbound access to OneLake with Resource Instance Rules. Data Engineering Native Execution Engine performance improvements This month, we continued to improve the Native Execution Engine (NEE) with a new set of query execution optimizations designed to accelerate Spark workloads while reducing compute consumption. Key enhancements include broadcast joining reuse across queries, native acceleration for ranking window functions such as RANK and DENSE_RANK, and automatic materialization of reused Common Table Expressions (CTEs). Together, these optimizations eliminate redundant computation, keep more processing within the engine's vectorized execution path, and improve performance for common data engineering patterns including large joins, analytical reporting, ranking workloads, and complex transformation pipelines. Because these improvements are enabled automatically when NEE is available, customers benefit from faster execution times, lower Fabric capacity consumption, and improved price-performance without requiring code changes to existing notebooks, Spark Job Definitions, or pipelines. These optimizations are enabled by default once the Native Execution Engine is turned on, allowing customers to realize performance gains immediately without additional configuration or tuning. Customers can enable NEE at the workspace environment level by navigating to Environment > Acceleration and turning on Native Execution Engine, ensuring it is available for all Spark sessions using that environment. It can also be enabled at the session level by setting spark.native.enabled=true in Spark configuration. With NEE enabled, customers can seamlessly take advantage of the latest runtime innovations to process data faster, improve resource efficiency, and maximize the value of their Fabric capacity investments. To learn more about Native Execution Engine explore our documentation Native execution engine for Fabric Data Engineering. Fabric Runtime 2.0 (Generally Available) As the execution foundation for Microsoft Fabric's Data Engineering and Data Science experiences, Runtime 2.0 delivers a modern, high-performance platform built on Apache Spark and deeply integrated across the Fabric ecosystem. Purpose-built for large-scale data processing and analytics workloads, Runtime 2.0 represents a major advancement in performance, reliability, security, and future readiness. Built on the latest open-source innovations, it enables customers to accelerate data processing, simplify operations, and take advantage of the newest capabilities across Microsoft Fabric. This release includes significant platform upgrades, including Apache Spark 4.1, Delta Lake 4.2, Python 3.13, Java 21, Scala 2.13, and Azure Linux 3.0, providing a modern and enterprise-ready foundation for the next generation of data engineering, data science, and analytics workloads. These enhancements enable customers to take advantage of the latest open-source innovations while continuing to benefit from a fully managed, enterprise-grade experience in Microsoft Fabric. Whether you're building data pipelines, developing AI and machine learning solutions, processing streaming workloads, or powering enterprise analytics, Runtime 2.0 provides a more capable, scalable, and performant platform for your workloads. With improved performance, updated open-source foundations, and continued investment in capabilities such as the Native Execution Engine, Runtime 2.0 provides a modern platform for data engineering, data science, and analytics workloads in Microsoft Fabric. Explore the full documentation and start using Runtime 2.0 in production Runtime 2.0 in Fabric. Enhanced Spark Properties Support in Notebook and Spark Job Definition Notebook and Spark Job Definition (SJD) activities now enable users to specify Spark properties directly within the data integration pipeline. This enhancement allows Spark properties to be set inside the activity panel, ensuring that the values provided are used for activity execution. If an Environment item is linked to the Notebook or SJD and both the activity panel and Environment item define the same property, the value specified in the activity panel will take precedence and overwrite the Environment value. In the case of Notebooks, if the %%configure command is used within the notebook code to set a Spark property, the value set using %%configure will be applied for execution. This update offers users maximum flexibility, allowing Spark properties to be defined at different layers based on their specific use cases. By supporting property configuration in the activity panel, Environment item, and notebook code, users can tailor property values to meet the unique requirements of each execution of Notebook/SJD. To learn more on Transform data by running notebook and Transform data by running a Spark Job Definition activity. Conversational Analytics Enhanced Data Agent Visualizations with Fabric Visuals The data agent now uses Fabric visuals to render the charts it returns, bringing higher-quality, more consistent visualizations into your conversations with your data. When you ask a question like "Generate a bar chart of revenue by region" or "Show me my top 10 customers by sales," the data agent responds with an interactive, polished visual alongside its text and table answers, so you can spot trends, comparisons, and outliers. Because the data agent now shares the same visual foundation as Fabric Apps, charts look and behave consistently with AI-generated visuals in Fabric, with refinements to formatting, legends, tooltips, and axis scaling. Supported chart types include line, bar, stacked bar, pie, scatter, and area charts. To learn more, refer to the Get visual responses from a Fabric data agent documentation. Advanced DAX Generation for Semantic Models in Data Agents Advanced DAX generation for Power BI semantic models is now available in Fabric data agents when you use the Preview runtime. Instead of generating a DAX query in a single pass, the new system works iteratively as a specialized sub-agent that can use tools, inspect results, and refine its approach across multiple steps, providing significant improvements in response accuracy. It also uses instance value indexing to resolve values from the semantic model before generating a query, resulting in more accurate and reliable filters. This update is built on the same semantic-model query engine used across Fabric Skills, Power BI, and M365 Copilot, providing more consistent answers across Microsoft experiences. To use the new experience, open the Runtime dropdown in the data agent ribbon and switch from Standard to Preview. More improvements for semantic models in data agents are coming soon, including data source description and instructions, granular schema selection, and example queries. In the Preview runtime, the data agent uses advanced DAX generation to answer a question over a connected semantic model, enabling more accurate DAX generation and responses. To learn more, refer to the Semantic model best practices for data agent documentation. Data Agent orchestrator upgraded to GPT 5.1 The data agent orchestrator now runs on GPT-5.1, across both the standard and preview runtimes. The orchestrator handles how questions are rephrased, how work is planned across your data sources, and how the final answer is composed — so this upgrade changes behavior in all three. Most of what you see should be an improvement in answer quality and planning, but the change is not behavior-neutral: prompts tuned against the previous model may produce different results. We recommend re-running your evaluations, reviewing the results against your saved baselines, and updating your agent instructions and prompts where the new behavior doesn't match what your scripts expect. To learn more, refer to the data agent runtimes documentation. Example Query Usability Improvements in Data Agent We've made several usability improvements, for example queries. Errors now surface inline, directly alongside the query, so you can identify and correct issues without leaving the editor. The editor also resizes automatically based on the length of your query, removing the need to adjust the pane manually as you write. To learn more, refer to the example queries in data agent documentation. Add Schema Descriptions for SQL Sources in Data Agent Users can now provide tailored schema descriptions through the new schema description editor, available for SQL sources on the Preview runtime. Schema descriptions improve query generation and accuracy by giving the agent more context about what everything in your data means — use them to resolve ambiguous columns, or to give a table or field a more precise meaning than its name conveys. Instead of inferring intent from column names alone, the agent works from what your data team documented, so it selects the right tables and interprets fields the way you intended. To learn more, refer to the schema descriptions documentation. Data Agent is migrating from Assistants API to Responses API The OpenAI Assistants API that powers the orchestration layer for the Microsoft Fabric data agent, is currently scheduled to be shut down by OpenAI on August 26, 2026. After that date, direct calls to the Assistants API will stop working. If you connect to a Fabric data agent programmatically through the Assistants API, you need to migrate to the data agent Model Context Protocol (MCP) endpoint. SDK and Fabric portal users require little or no action because Microsoft will migrate those experiences internally, although conversation history may reset once. Existing agent data sources, instructions, and tools remain unchanged. To learn more, refer to Prepare your Fabric Data Agent integrations for Assistants API retirement. Fabric Data Agents in Microsoft Copilot Studio (Generally Available) Now, you can bring governed business data from Microsoft Fabric into Copilot Studio agents, so those agents can answer questions and support business processes using trusted enterprise data. Since preview, the integration has moved to the new tool-based experience: select Add a tool, search for Fabric, and add Fabric IQ Data MCP, and your agent can call the Fabric data agent like any other tool. The Fabric data agent still runs in Fabric and respects permissions on the underlying data sources. You can also publish your agent to Microsoft Teams and Microsoft 365 Copilot, so business users get data-grounded answers where they already work. To learn more, refer to the Fabric Data Agent MCS GitHub documentation for setup steps and join the community discussion to share feedback. Fabric data agents in Microsoft Foundry: Easier to connect, easier to trust Fabric data agents in Microsoft Foundry are now easier to connect and easier to monitor. The integration moves to Model Context Protocol, so your Fabric data agents appear as tools that Foundry agents can invoke when they need enterprise data in OneLake. Connecting them no longer means hunting for workspace and artifact IDs. You add the Fabric IQ (OneLake Catalog) tool, filter for data agents, and pick the ones you want by name. You can also connect more than one Fabric data agent to a single Foundry agent, so an agent can draw on a sales agent, a supply chain agent, and a customer support agent and choose the right one for the question. On the operations side, you can now view logs and traces for Fabric data agents through Foundry Observability. Traces show which tools were invoked, how long each step took, and what came back, which makes it much easier to troubleshoot an answer that looks wrong or a workflow that runs slow. This is the visibility teams need to move agents from experiments to production. The update is rolling out to all regions over the coming days. Add Fabric data agent as part of Fabric IQ to your Foundry agent. To learn more, refer to the Observability for Fabric data agents in Microsoft Foundry documentation. Add co-publishers for data agents in Microsoft 365 Copilot When you publish a Fabric data agent to Microsoft 365 Copilot, the Microsoft 365 agent platform registers the agent and records you as its only owner. That created a problem for teams. Your co-creators could still edit the data agent in Fabric, but when they tried to republish it, the operation failed, because Microsoft 365 only lets registered owners publish. Fabric access and Microsoft 365 ownership are two separate lists, so giving someone edit rights in Fabric was never enough. Now, you can avoid this with co-publishers. After you publish the data agent, open Settings, go to the Publishing pane, and add your Fabric co-creators under Microsoft 365 Copilot co-publishers. Each person you add is registered as a co-owner on the Microsoft 365 agent platform, so anyone on that list can republish the agent. Add co-publishers right after your first publish so no one hits a failure in the meantime. Publishing pane in the Fabric data agent settings, showing where you add Microsoft 365 co-publishers. To learn more, refer to the Consume a data agent from Microsoft 365 Copilot (preview) documentation. Data Warehouse Identity columns with identity insert (Generally Available) Since preview, thousands of customers have adopted IDENTITY to auto-generate surrogate keys and streamline migrations from SQL Server, Azure SQL Database, and Azure Synapse. Now we're introducing support for IDENTITY_INSERT and reseed operations - two highly needed additions - so you can insert explicit key values, migrate data in bulk with COPY INTO, and safely realign identity ranges with DBCC CHECKIDENT. Figure: Using identity insert on Fabric Data Warehouse. IDENTITY columns are available now in every Fabric Data Warehouse. To learn more, check our updated tutorial and documentation. CI/CD 2.0 with DacFx (Preview) Microsoft Fabric Data Warehouse is introducing a major update to the DacFx engine that powers schema comparison, Git integration, and deployment pipelines. DacFx builds a declarative model of your warehouse and determines the schema changes required to move safely between development, test, and production environments. Git-integrated CI/CD workflow for Microsoft Fabric, showing feature workspace synchronization, branch merging, and deployment pipeline promotion across development, test, and production workspaces. With this update, Git integration uses DacFx-based incremental extraction to produce cleaner, more focused commits. Deployment pipelines also use the updated model to generate more accurate comparisons and smarter deployment plans, with settings tuned for schema evolution. The new warehouse item definition version 2.0 updates the SQL project SDK, moves shared queries into a .sharedqueries folder, adds project-level Git configuration, and re-extracts object definitions to support constraints, identity columns, clustering, and consistent formatting. These changes make future commits easier to review and reduce noisy diffs. For more information, refer to the Upgrade Fabric Data Warehouse System File Version in a Git Integrated Fabric workspace documentation. Microsoft Fabric source control notification prompting users to apply the latest Warehouse system update, with a warning that the update will introduce differences between the workspace and its connected Git repository. The update also improves comparison accuracy. Git-connected workspaces can adopt the update when ready through the System update available experience, giving teams control over upgrade timing. Review and commit the generated changes before continuing normal development and deployment workflows. To learn more, refer to the Development and Deployment Overview documentation. Simplify Fabric Warehouse deployments with Schema Compare in VS Code Database deployments should not feel like a guessing game. With Schema Compare in Visual Studio Code, developers can see exactly what changed before those changes reach a Fabric Warehouse—bringing clarity and control to every release. Compare a Fabric Warehouse with another warehouse or a SQL database project, then review differences across tables, views, stored procedures, functions, and other database objects in a clear, object-by-object view. Choose the changes you want, update the project from the warehouse, or deploy selected changes to the target—without manually assembling and reviewing every deployment script. By keeping database projects synchronized in Git, teams gain a reliable source of truth and can bring schema changes into familiar pull-request and CI/CD workflows. The result is a safer, more intentional path from development to production, with fewer surprises at deployment time. Before applying changes, review the generated script for unsupported operations and potential data loss. To learn more, refer to the Develop warehouse projects in Visual Studio Code and Schema Compare in the MSSQL extension documentation. GPU Query Acceleration (Preview) Query Acceleration brings GPU-powered performance directly to Fabric Data Warehouse, enabling eligible analytical queries to run faster without query rewrites, special syntax, or additional systems to manage. Query Acceleration in Fabric Data Warehouse uses GPUs to accelerate the most compute-intensive portions of analytical queries, helping overcome the limits of CPU-only execution. It works transparently with existing T-SQL, Direct Query reports, applications, and tools, automatically offloading eligible operations such as scans, filters, joins, and aggregations to GPUs while the CPU continues to manage the rest of the execution pipeline. Customers can use Query Insights, Data Warehouse Monitoring, and SQL Server Management Studio (SSMS) for query execution plans to identify accelerated queries and understand how Query Acceleration is applied during query execution. Designed for analytical and high-concurrency workloads, Query Acceleration can improve throughput, reduce query latency, and deliver more consistent performance for dashboards and interactive analytics. Acceleration is applied selectively, enabling performance gains even when only part of a query is eligible for GPU execution. The capability is built with reliability in mind. Unsupported operations or runtime constraints can seamlessly fall back to CPU execution without affecting query correctness. Performance improvements depend on workload characteristics, but Microsoft benchmarks have demonstrated gains of up to 7× across reporting, application, and AI-driven analytics scenarios. Query Acceleration builds on Microsoft's Tensor Query Processor research, described in CoddSpeed: Hardware Accelerated Query Processing in Microsoft Fabric, which was selected as the SIGMOD Companion 2026 Best Industry Paper. To sign up for the Preview, please fill out the form. Metadata Sync supports Delta Checkpoint V2 (Generally Available) Metadata Sync (MD Sync) now supports Delta Checkpoint V2, enabling synchronization of modern Delta tables across both MD Sync (Legacy) and MD Sync (New). Delta Checkpoint V2 is a Delta Lake enhancement designed to improve scalability for large tables through a more efficient checkpoint structure. Previously, tables using Checkpoint V2 couldn't be synchronized and were reported as unsupported. With this release, MD Sync can discover and synchronize Delta tables that use the Checkpoint V2 format. This enhancement helps customers: Synchronize Delta tables that use Checkpoint V2. Improve interoperability with Spark, Databricks, and other Delta-based platforms. Support metadata synchronization for large-scale Delta tables more efficiently. Continue using existing checkpoint formats without any changes. MD Sync support for Delta Checkpoint V2 is available in both MD Sync (Legacy) and MD Sync (New), helping ensure consistent access to Delta tables across Fabric experiences. Secure data ingestion with COPY INTO and Workspace Identity (Generally Available) COPY INTO in Fabric Data Warehouse now supports Workspace Identity, enabling users to load approved data from OneLake or ADLS Gen2 without requiring direct access to the source files. Previously, ingestion users often needed permissions to both the target warehouse and the source storage location, or teams relied on SAS tokens, account keys, or service principals. With this release, source access can be centrally assigned to the workspace identity, while users retain only the SQL permissions required to load data into the target table. Key Capabilities: Load approved data without granting users direct access to raw storage. Use managed identity-based authentication for OneLake and ADLS Gen2 sources. Reduce reliance on SAS tokens, shared keys, and service principal secrets. Maintain separate authorization boundaries for source access and target-table permissions. Support least-privilege ingestion and separation of duties between storage and warehouse administrators. Workspace Identity support for COPY INTO is generally available in Fabric Data Warehouse, providing a simpler and more governed approach to secure data ingestion. To learn more, refer to the Ingest Data into Your Warehouse Using the COPY Statement and COPY INTO (Transact-SQL) documentation. SQL Audit Logs: More Signal, Less Noise with Predicate Filtering (Generally Available) SQL Audit Logs in Fabric Data Warehouse and SQL Analytics Endpoint now support identity-based predicate exclusion filtering, enabling administrators to reduce repetitive audit events generated by selected users and service principals. Previously, expected activity from automation identities, scheduled processes, metadata synchronization jobs, and other operational actors could create significant audit noise. With this release, administrators can configure exclusions through the API or SQL Audit Logs user experience, while activity from identities that do not match the exclusion predicate continues to be audited normally. Key Capabilities: Reduce repetitive audit events from known users and service principals. Focus investigations on higher-value and unexpected activity. Lower the storage, processing, export, and query burden associated with low-value events. Manage identity exclusions through either automated APIs or the user experience. Apply a governed audit policy aligned with organizational monitoring and compliance requirements. Identity-based predicate exclusion filtering is generally available for SQL Audit Logs in Fabric Data Warehouse and SQL Analytics Endpoint, providing a cleaner audit stream, less operational overhead, and more focused investigations. To learn more, refer to the SQL Audit Logs in Fabric Data Warehouse documentation. OneLake security improvements for SQL analytics endpoints (Generally Available) OneLake Security for SQL analytics endpoints now includes improvements for nested groups, shortcut-backed tables, column-level security, and service principals, enabling more consistent enforcement of OneLake security policies across enterprise Fabric environments. Previously, limitations with group expansion, shortcut scenarios, and service principal ownership could make centralized security difficult to apply at scale. With these improvements, customers can define security at the source lakehouse and rely on the SQL analytics endpoint to honor those policies across producer and consumer workspaces. Key Capabilities: Manage access through nested Microsoft Entra group hierarchies. Honor source-side OneLake Security policies for shortcut-backed tables in hub-and-spoke architectures. Apply column-level security consistently when users receive access through groups. Use service principals for automated deployments, pipelines, and application-owned data products, including service principal-owned lakehouses. Define security once in OneLake and reduce the need to duplicate permissions across consumer workspaces and Fabric engines. These OneLake Security improvements help make security synchronization more practical for enterprise architectures while providing consistent access control across lakehouses and SQL analytics endpoints. Microsoft is also continuing to improve security sync notifications, error handling, and permission propagation across Fabric experiences. To learn more, refer to the OneLake Security for SQL analytics endpoints documentation. Real-Time Intelligence Set Alerts Directly from Anomaly Detector (Generally Available) Detecting anomalies becomes more valuable if you can act on them. Previously, after publishing an anomaly detector configuration, you had to leave Anomaly Detector and navigate to Real-Time Hub to create an alert. This added extra steps and interrupted your workflow right after completing your configuration. With this update, you can now create alerts directly from Anomaly Detector. Once you publish a configuration, use the Set alert button in the ribbon to launch the alert creation pane without leaving Anomaly detector. If your configuration hasn't been published yet, you'll be guided through publishing first and then taken directly to the alert setup experience. This helps you move seamlessly from configuring anomaly detection to monitoring it in production. The integrated experience allows you to monitor your anomalies on each event, helping you get notified as soon as anomalies are detected. If you have more complex business logic, select on each event when to add in additional logic to your conditions. Whether you're monitoring operational metrics, business KPIs, or real-time telemetry, you can now complete the entire workflow in one place and start acting on detected anomalies faster with fewer clicks. Create alerts directly from your anomaly detector configuration and continue your workflow without navigating to another experience. Configure notifications for anomaly detector events directly within Anomaly Detector and start monitoring your published configuration immediately. Anomaly detector supports Eventhouse shortcut tables Anomaly Detector now supports Eventhouse shortcut tables, making it possible to analyze data without first copying or moving it into a dedicated Eventhouse table. You can create anomaly detectors directly on supported shortcut tables and use the same analysis, model recommendations, and continuous monitoring experiences available for native Eventhouse data sources. This expands anomaly detection to a broader range of data already connected through Eventhouse shortcuts, helping teams monitor external and federated data sources with less setup and duplication. By enabling anomaly detection directly on shortcut tables, you can move more quickly from connecting data to detecting issues, while continuing to work within a unified Real-Time Intelligence experience. To learn more, refer to the Anomaly Detection in Real-Time Intelligence documentation. Operations Agent Activity Log Understanding what your agent is doing and why is key to building trust and improving outcomes. The activity log is designed to provide that transparency. It gives you a clear view into the agent’s behavior, including the conditions it evaluated, the recommendations it generated, and how those recommendations were handled. Whether you are validating results, troubleshooting unexpected behavior, or refining your configuration, the activity log helps you better understand how decisions are being made. You can access the activity log from the Activity log section in the side navigation. It presents a chronological timeline of events with timestamps and relevant context for each entry. Selecting any event allows you to explore additional details and understand what happened at each step. In the Operation details page, you can view the operation details and status. To learn more, refer to the Create and Configure Operations Agents documentation. Eventstream MQTT connector (Generally Available) It is now easier than ever to ingest real-time data from MQTT brokers directly into Microsoft Fabric Real-Time Intelligence. MQTT is one of the most widely adopted messaging protocols for lightweight, low-bandwidth event driven messaging scenarios. Eventstream MQTT connector simplifies the ingestion of operational and IoT data into Microsoft Fabric, helping organizations turn real-time device events into actionable insights. Key Benefits: Connect to any MQTT broker and ingest messages directly into Fabric Eventstream. Production-ready reliability and support with General Availability readiness. Enterprise-grade security with support for TLS, mutual TLS (mTLS), and custom certificate authorities managed through Azure Key Vault. Private network connectivity through Eventstream's streaming connector virtual network capabilities, enabling secure access to brokers hosted in private and on-premises environments. To learn more, refer to the Add MQTT source to an eventstream documentation. Reference data enrichment in Eventstream (Preview) Eventstream now enables you to enrich real-time event streams with contextual business data using Reference Data Join. Simply add a Reference Data node to your Eventstream, select a Delta table from a Fabric Lakehouse, and use it to enrich streaming events with lookup, metadata, or reference information. You can also leverage Lakehouse shortcuts to access Delta tables across OneLake, making it easy to bring contextual data from anywhere in your Fabric environment into your real-time processing pipelines. Reference Data Join supports both no-code and SQL-based enrichment experiences. Use the built-in Join operator to visually configure INNER and LEFT OUTER joins or use the SQL operator for advanced scenarios. Select only the columns you need from the reference dataset and configure optional refresh intervals to keep slowly changing reference data up to date. This enables Eventstream to continuously use the latest lookup information for real-time enrichment, without requiring additional data movement or downstream processing pipelines. You can easily add multiple reference data sources to a single Eventstream and combine them with streaming data to create richer, more contextual event pipelines. Developers and data engineers can test and validate join conditions, preview join results, and verify SQL-based enrichment queries before deploying them into production, helping ensure accuracy and confidence in real-time data processing workflows. Reference Data Join unlocks powerful real-time enrichment scenarios in Eventstream. Users can enrich IoT telemetry with device metadata, correlate operational events with customer and product information, perform lookups against business reference datasets, and add contextual information to streaming data in flight. By bringing reference data and event processing together in a single experience, Eventstream enables customers to transform raw events into actionable business insights in real time. To learn more, refer to the Reference data join in Eventstream using Lakehouse documentation. Eventstream observability in Workspace Monitoring re-enabled with per-Eventstream control (Preview) Eventstream observability in Workspace Monitoring is back — now with granular control over which Eventstreams emit monitoring data. A new ‘Log Eventstream activity’ toggle in Eventstream Settings lets you enable or disable observability per Eventstream, so you can balance monitoring coverage with capacity consumption. When enabled, your Eventstream emits performance metrics, error counts, and health status to three tables in your Workspace Monitoring Eventhouse: EventStreamMetrics: throughput, backlog, and watermark delay EventStreamErrorMetrics: deserialization, conversion, and runtime error counts EventStreamNodeStatus: node health (Running / Failed) The toggle defaults to OFF for all Eventstreams. To get started, open any Eventstream, go to Settings, and turn on Log Eventstream activity. Your monitoring data will appear in the Workspace Monitoring database within minutes. Eventstream activity" within Monitoring. The panel highlights an active toggle switch, a description explaining that enabling this feature emits performance and error metrics to a monitoring database. To learn more, refer to the Monitor Eventstream data flows in Workspace Monitoring documentation. Eventstream UI editor improvements (Preview) We've redesigned key parts of the Eventstream editor to make building and troubleshooting faster and more intuitive. Always Publish: No more blocked publish buttons. Publish your work at any stage, the editor gives you clear, contextual guidance on what still needs attention instead of preventing you from moving forward. Inline error indicators: Errors now appear directly on the node that needs fixing, with actionable guidance on click. No more hunting through a detached error list to find what's broken. Operator and destination descriptions: Each option now includes an inline description explaining what it does, so you can build confidently without switching to docs. These changes reduce friction during authoring and make it easier to go from idea to running your pipelines. Secure Azure Event Hubs Connections in Eventstream with Workspace Identity (Preview) Bringing real time event data into Microsoft Fabric is now simpler and more secure with Azure Event Hubs integration for Eventstream. Organizations can connect Event Hubs directly to Eventstream and start routing event data to destinations such as Eventhouse for analytics and operational insights. A key capability is Workspace Identity, which removes the need to manage shared access keys. Instead, Eventstream can authenticate to Azure Event Hubs using the Fabric workspace identity. Administrators simply grant the workspace the Azure Event Hubs Data Receiver role, enabling secure access through Microsoft Entra based permissions. This approach improves security, simplifies credential management, and aligns with enterprise governance requirements. The integration supports both public and private network deployments. For Event Hubs hosted in private networks, organizations can connect through a streaming virtual network gateway while continuing to use Workspace Identity for authentication. For advanced event processing scenarios, users can enable schema support, associate schemas from the event schema registry, and route structured events to destinations such as Eventhouse. Combined with Workspace Identity, this provides a secure and scalable foundation for building real time data pipelines without the operational overhead of managing secrets or credentials. Schema Registry and Event SchemaSet Region Availability Previously, Schema Registry feature and the Event SchemaSet artifact was available for preview in 31 regions. Expanded to 10 additional regions: Geography Region Americas Central US Americas Mexico Central Americas West US 3 Europe Italy North Europe Poland Central Europe Spain Central Europe West Europe Asia Pacific Australia Southeast Asia Pacific Israel Central Asia Pacific Japan West If you were previously blocked from trying SchemaSets and schema-based Eventstream data ingestion, you can now do so in these regions. For more information on Event SchemaSets and how you can create and manage them, visit Schema Registry Overview. To learn more about configuring Eventstreams with schema-based sources, visit Use schemas in Eventstreams. Data-driven styling and UX improvements for Maps (Generally Available) Maps become most valuable when they help users understand not just where things are, but what the data means. Now, Data-Driven Styling, along with Markers Rotate by Data, Traffic Flow visualization, additional Map View options, and an improved Layer Settings experience. Together, these enhancements help organizations transform raw geospatial data into intuitive, actionable business insights. Let Your Data Tell the Story Understanding patterns hidden within geographic data can be challenging when every feature on a map looks the same. With Data-Driven Styling, Fabric Maps enables map builders to visually represent business data directly on the map, helping viewers quickly identify trends, hotspots, and outliers without inspecting individual records. The new Color by Value Range capability allows authors to style map layers using numeric measures such as revenue, utilization, sensor readings, environmental measurements, or operational KPIs. Instead of applying a single color to an entire layer, Fabric Maps visualizes value distributions chromatically, making important differences immediately visible. Organizations can choose between two visualization approaches: Gradient Styling uses continuous color transitions to reveal magnitude, trends, and geographic variation across a dataset. Step-Based Styling allows users to define custom value ranges with distinct colors, making it easy to visualize business thresholds, risk levels, performance bands, or service categories. To make these visualizations easier to interpret, Fabric Maps automatically generates corresponding data legends that explain how colors map to underlying values. This helps viewers understand the meaning behind the visualization and make decisions with greater confidence. Fabric Maps also includes thoughtfully designed color palettes, including options that support colorblind-friendly visualization scenarios, helping more users accurately interpret map-based insights. Combined with clear, automatically generated legends, these capabilities improve accessibility and make data-driven maps easier to understand across a wider range of audiences. per street and road and high-value exposure in flood-prone areas. Visualize Direction and Movement with Markers Rotate by Data Many operational scenarios involve not only location but also direction. Fabric Maps now supports Markers Rotate by Data, allowing marker symbols to automatically rotate based on values stored in a data column. This capability is available for Marker layers, enabling builders to visualize directional information directly on the map. Whether visualizing aircraft headings, vehicle movements, equipment orientation, or other operational workflows, map authors can represent direction without requiring custom visualization development. By transforming static points into directional indicators, organizations can add valuable operational context and communicate movement patterns more effectively. Add Real-World Context with Traffic Flow Visualization Location data alone doesn't always tell the full story. Fabric Maps now supports Traffic Flow overlays, allowing map builders to bring current traffic conditions into their existing map experiences. By combining business data with real-world traffic information, organizations can gain additional situational awareness for logistics operations, field service planning, transportation monitoring, and operational decision-making. The added context helps users better understand the environment surrounding their assets and activities without leaving the map experience. Configure the Right Map View for Your Audience Organizations often create maps for users across different regions and business contexts. Fabric Maps introduces additional Map View settings that allow map builders to configure how geographic information is presented, helping ensure maps are displayed in a way that aligns with organizational needs and audience expectations. This flexibility gives authors greater control over creating a consistent and intuitive viewing experience across a variety of business scenarios. A More Discoverable Authoring Experience UX research and user feedback showed that some key layer settings were difficult to discover. We updated the experience by moving geometry and visualization options—including visual type, Data-Driven Styling, and marker rotation—higher in the configuration pane. We also renamed General to Visibility, making the settings clearer and map authoring more intuitive. Turn Location Data into Business Insight Data-Driven Styling, built-in data legends, Markers Rotate by Data, Traffic Flow overlays, enhanced Map View options, and the improved Layer Settings experience help organizations transform location data into meaningful business insight. Together, these capabilities make it easier to uncover patterns, understand operational context, and communicate geospatial insights across teams. Start building with these capabilities today: explore the customization options, apply them to your own geospatial data, and create map experiences that turn location into action. To learn more, refer to the Customize a map in Microsoft Fabric documentation. Workspace Outbound Access Protection (OAP) for Operations Agent (Preview) Workspace Outbound Access Protection (OAP) in Microsoft Fabric helps admins secure outbound connections from workspace items to external resources. Admins can control outbound access by blocking unwanted connections by default and allowing only approved connections through configured rules. As organizations adopt AI-powered operations at scale, governance and security remain critical requirements. With this preview release, Microsoft Fabric introduces Outbound Access Protection (OAP) for Operations Agent, enabling workspace administrators to control the outbound actions an agent can perform. OAP applies workspace-level policies to actions such as Teams notifications, workflow triggers, and cross-workspace operations, helping organizations enforce security and compliance requirements while continuing to benefit from AI-driven automation. When OAP is enabled, Operations Agent continues to perform core functions including reasoning, recommendation generation, rule evaluation, and telemetry collection. However, outbound actions are governed by the workspace's configured access policies. Administrators gain greater visibility through in-product notifications, Teams messaging experiences, and the Operations Agent Activity Log, making it easier to identify and troubleshoot blocked actions What's new with Operations Agent and OAP? Govern outbound agent actions through workspace-level OAP policies. Control whether Operations Agent can send Teams notifications based on allowed connections. Prevent unauthorized cross-workspace actions when OAP policies restrict outbound access. Receive clear visibility when actions are blocked through in-product notifications and Teams messaging experiences. Monitor agent activity and OAP-related outcomes through the Operations Agent Activity Log. During the preview, some limitations apply. Cross-workspace actions are blocked when OAP is enabled. For example, Power Automate actions are not yet supported when OAP is applied, and only connectors that explicitly support OAP policies can be permitted. By extending Fabric's outbound governance framework to Operations Agent, organizations can adopt AI-powered operational automation with greater confidence while maintaining control over how and where agent-initiated actions are executed. Resources Workspace outbound access protection for operations agent (preview) Workspace Outbound Access Protection (OAP) Workspace Outbound Access Protection for Operations Agents Configure and manage Activator rules directly in Eventstream (Generally Available) You can now create and manage rules directly in Eventstream. Previously, setting up an alert required switching from Eventstream to Activator. While powerful, this meant moving between experiences to complete a single workflow. Now, alert creation is embedded directly into Eventstream. Capabilities for building or editing your Eventstream: Select the stream you want to monitor. Choose Set Alert. Define your condition (thresholds, aggregations, patterns). Configure the action. Create the rule. Capabilities for Activator destination created on Eventstream: Select Activator node Select Rule icon Create the rule Once you have the rule(s) created on your Eventstream, you can manage them by editing, deleting or opening in Activator. To learn more, refer to the Add a Fabric activator destination to an eventstream documentation. Data Factory Introducing Hierarchical Navigation in Monitoring Hub for Fabric Pipelines Modern data estates rarely consist of a single job running in isolation. Pipelines trigger notebooks, notebooks invoke other workloads, and business processes span multiple interconnected executions. When troubleshooting a failure or understanding lineage, customers often need visibility into how these executions relate to one another. Hierarchical Navigation in Monitoring Hub, is a new capability that helps you understand the relationships between runs and quickly navigate across upstream and downstream executions. With Hierarchical Navigation enabled, Monitoring Hub can display: Upstream runs that initiated a workload Downstream runs triggered by a workload Execution relationships across supported Fabric artifacts This provides a richer observability experience by helping you move beyond individual run monitoring and understand how your workloads operate together. This enhancement is another step toward a richer observability experience in Fabric, helping customers gain deeper insight into workload execution and dependencies at scale. To learn more, refer to the Hierarchical Navigation for Pipelines in Monitoring Hub documentation. Explore the modern Fabric Pipeline canvas (Preview) The new Fabric Pipeline canvas experience is designed to make pipeline authoring easier than ever. Key capabilities with modern canvas: Better visibility when navigating large pipeline graphs Cleaner, more structured layouts for complex orchestration logic Improved responsiveness when working with enterprise-scale workflows A more intuitive experience for pipelines with many activities and branches The modern Fabric Pipeline canvas, showing the updated node experience and option to disable the preview if needed. If you haven't tried it yet, now's the perfect time. The new experience is rolled out automatically and can be disabled at any time. Whether you're building your first pipeline or managing hundreds of activities across complex workflows, the new canvas is designed to help you stay productive and focused on what matters most. To learn more, refer to the Modern Pipeline Node Experience documentation. Upgrade Dataflow Gen1 to Dataflow Gen2 (CI/CD) using the Upgrade Wizard (Preview) The Dataflows Upgrade Wizard is a guided, end-to-end experience that upgrades your existing Power BI Dataflow Gen1 items to Dataflow Gen2 (CI/CD) in Microsoft Fabric with minimal effort. You can upgrade a single dataflow, or several dataflows from a workspace in a single flow. Previously, bringing a Gen1 dataflow into Fabric meant recreating it and repointing everything that depended on it. The Upgrade Wizard upgrades in place instead. Each dataflow keeps its ID, name, schedule, and connections, so the reports and semantic models that connect to it keep working without any changes. Before anything changes, the wizard assesses every dataflow in the workspace and tells you which ones need attention and why, such as incremental refresh settings to reconfigure or a linked entity to update, so you know what to expect before you upgrade. Why this matters Upgrade in place, with nothing to rebuild and nothing to repoint. Upgrade a whole workspace at once instead of one dataflow at a time. See what needs attention before you upgrade. Unlock Dataflow Gen2 innovations: improved performance, deeper Fabric integration, CI/CD and Git support, data destinations of your choice, richer diagnostics, Copilot-assisted authoring, and a modern data transformation foundation. The wizard is available for Dataflow Gen1 items in Premium or Fabric workspaces and requires Fabric to be enabled. To learn more, refer to the Upgrade Dataflow Gen1 to Dataflow Gen2 (CI/CD) using the Upgrade Wizard documentation. Extended watermark support in Copy job Watermark-based incremental load support enables Copy Job to efficiently ingest only new or changed data from key enterprise and SaaS data sources, avoiding costly full reloads. This reduces source-system impact, network traffic, and runtime while improving scalability for production analytics workloads. Customers can now use incremental loading across more of their critical data sources, including Salesforce, Informix, Cassandra, Greenplum, Presto, and Databricks, by leveraging Copy job’s built-in watermark mechanism and without building custom ingestion logic. To learn more, refer to the Incremental copy in Copy job documentation. Enable Change data feed during Lakehouse table creation in Copy job Copy job can now create Lakehouse tables with Change Data Feed (CDF) enabled automatically. There’s no longer a need to pre-create destination tables or manually configure Delta table properties. Simply select Enable CDF on destination and Copy job takes care of the setup for you. This ensures the table is immediately ready for incremental processing and downstream CDC scenarios, helping reduce data movement and improve replication efficiency. By eliminating manual configuration steps, it makes advanced data integration patterns much easier to adopt and operate at scale. To learn more, refer to the Automatic table creation and truncation on destination documentation. Amazon Redshift as new source in Copy job As part of our mission to enable multi-cloud data movement at petabyte scale with Copy job, we are bringing Amazon Redshift support as a source. This enables customers to seamlessly ingest data from one of AWS's most widely adopted data warehouse platforms directly into Fabric. Redshift support further strengthens Fabric's vision of delivering an open, connected, and multi-cloud data platform. To learn more, refer to the Connectors for Copy Job documentation. Copy job supports timestamps without time zone in Lakehouse Support for timestamps without time zones (timestamp_ntz) allows Fabric Lakehouse tables to preserve date and time values exactly as stored, without applying time zone conversions. Copy job can now automatically map timezone-independent datetime values to Delta Lake timestamp_ntz, ensuring greater compatibility with source systems that allow storing date and time values without time zone information. Migration Assistant for SQL database in Fabric (Generally Available) The guided, Fabric-native wizard takes you from a source SQL Server schema to a running SQL database in Fabric: upload a DACPAC, review compatibility results, deploy the schema with Copilot-assisted fix suggestions, and copy your data using built-in Fabric Copy Jobs. We've added the capability preview customers asked for most: Validate. You can now check out a DACPAC for compatibility before creating a SQL database in Fabric. Upload the file and the assistant reports which schema objects will deploy cleanly, which ones use features that aren't supported, the reason behind each failure, and the dependencies between objects — with nothing provisioned and no capacity consumed. That means you can scope migration effort, plan remediation, and get change-approval sign-off before you commit to a target database. To get started, select Migrate in your Fabric workspace and choose Migrate to SQL database in Fabric. To learn more, refer to the Fabric Migration Assistant documentation. Until next month That's a wrap for the August 2026 Microsoft Fabric Monthly Update. As always, we'll continue sharing new capabilities, enhancements, and improvements across Microsoft Fabric in future monthly updates. Thank you for being part of the Fabric community!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.Building the Bronze → Silver → Gold layers
Part two of a series on medallion architecture with Fabric Data Warehouse One job per layer — with enough implementation detail to make it real In Part 1 of this series, you picked your pattern. Now, let’s fill in the layers. The single most useful mental model here: each layer has exactly one job. Most medallion messes come from a layer doing another’s work — cleaning data in Bronze or letting business logic creep into Gold. In this post, we’ll look at hose Bronze, Silver, and Gold layers are implemented in Fabric Data Warehouse (DW), the T-SQL patterns commonly used in each layer, and the practices that help keep your architecture maintainable as it grows. We’ll also cover which practices are worth calling out before we move deeper into best practices in Part 3 of this series. Here’s what “one job” means in Fabric DW. Bronze — land it, don’t touch it Bronze has one job: ingest all source data in its original raw form, with no business logic or cleansing applied — “write everything down first” — so you preserve a source-of-truth copy you can always refer back to. In Fabric DW, that usually means staging tables that closely mirror the source structure, placed in a dedicated Bronze schema, such as Bronze.SalesOrdersRaw, to clearly mark them as raw. If the source is a relational extract, the Bronze table often follows the source columns. If the source is semi-structured, keep parsing to the minimum needed to land and trace the data. For loading, Fabric DW supports Data Factory Pipelines, Dataflows, COPY INTO, T-SQL ingestion, OPENROWSET, and Spark-based patterns. A common and efficient path is the T-SQL COPY INTO command to bulk-load files from OneLake or external storage into a DW table. Implementation sketch: CREATE SCHEMA Bronze; CREATE TABLE Bronze.CustomerRaw (CustomerID INT NULL, Name VARCHAR(100) NULL, Email VARCHAR(100) NULL, CreatedDate DATETIME2 NULL, RawFileName VARCHAR(255) NULL ); COPY INTO Bronze.CustomerRaw FROM 'https://<storage>/exports/customers/*.csv' WITH (FILE_TYPE = 'CSV', FIRSTROW = 2, FIELDTERMINATOR = ','); Hybrid note: if the raw data needs Spark-heavy preparation, land it in a Lakehouse for Bronze and then either expose it to the warehouse or begin Silver in Fabric DW. The rule does not change: Bronze is still raw, traceable, and rebuildable. Do this well: Preserve raw data. Do not filter out “bad” records in Bronze; all cleaning happens in Silver. Use batch loads, not row-by-row inserts. Small trickle inserts create many small Delta files and hurt performance. Keep file and schema discipline. Match the source structure and handle schema evolution by adding nullable columns rather than dropping source information. Add metadata columns like ingestion timestamp or source filename for traceability and auditing. Automate recurring loads with Fabric Pipelines or scheduled SQL scripts, so Bronze remains repeatable. Silver — Clean it once, for everyone Silver’s one job is to take raw Bronze data and apply cleaning, validation, and integration. This is where you remove duplicates, standardize formats, handle missing or invalid values, join related sources, and apply shared business rules. In Fabric DW, Silver is typically implemented with T-SQL transformations from Bronze into curated Silver tables. Use CTAS, or CREATE TABLE AS SELECT, when you want to materialize a clean table from a query. Use INSERT...SELECT or MERGE when the pattern is incremental. The important design point: Silver should become the single source of truth for cleansed data in the pipeline. You might keep one Silver table per Bronze source or combine multiple Bronze inputs into one conformed Silver table, such as a consolidated customer table. Implementation sketch: CREATE SCHEMA Silver; CREATE TABLE Silver.CustomerCleaned AS SELECT CustomerID, Name, IIF(Email NOT LIKE '%@%.%', NULL, Email) AS Email_Valid, CONVERT(DATETIME2(6), CreatedDate) AS CreatedDate FROM ( SELECT *, ROW_NUMBER() OVER (PARTITION BY CustomerID ORDER BY CreatedDate DESC) AS rn FROM Bronze.CustomerRaw ) AS t WHERE rn = 1; Hybrid note: if Bronze lives in a Lakehouse, Silver can be handled with Spark, Dataflows, or T-SQL after the data is exposed to the warehouse. Use Dataflows for lighter citizen-developer transformations; use Spark or T-SQL when scale, repeatability, or engineering complexity matters. Do this well: Make transformations idempotent, so they can run repeatedly without damaging data. Validate and enforce quality here. Silver is the gate where bad data gets stopped, fixed, or flagged. Use MERGE for incremental upserts when late-arriving data needs to update Silver instead of forcing a full reload. Use staging or temporary tables when the logic gets complex; simpler modular SQL is easier to maintain. Keep performance visible. Complex joins and aggregations belong here, but they should be written in a way the warehouse can optimize and the team can reason about. Gold — Shape it for the question Gold’s one job: present business-ready data that Power BI, dashboards, and downstream analytics can use directly. This is usually where you shape the model into facts and dimensions, data marts, wide reporting tables, or pre-aggregated summaries. In Fabric DW, this is where the warehouse shines. Gold tables are built with SQL transformations from Silver, often involving joins, calculated fields, and aggregations. The payoff is that report authors and business users do not need to repeat heavy transformations in every semantic model or dashboard. To create Gold tables, start from clean Silver data and optimize for the business question. For example, transaction-level Silver data can become a daily sales summary table used directly by Power BI. Implementation sketch: CREATE SCHEMA Gold; CREATE OR ALTER VIEW Gold.v_Customer AS SELECT CustomerID, Name, Email_Valid AS Email, CreatedDate FROM Silver.CustomerCleaned; CREATE TABLE Gold.DailySalesSummary AS SELECT CAST(s.OrderDate AS date) AS OrderDate, COUNT(DISTINCT s.OrderID) AS TotalOrders, SUM(s.TotalAmount) AS TotalSalesAmount, COUNT(DISTINCT s.CustomerID) AS UniqueCustomers FROM Silver.SalesCleaned AS s GROUP BY CAST(s.OrderDate AS date); Consumption: once Gold tables or views are created in Fabric DW, they are directly queryable by BI tools. Because the data is clean, aggregated, and shaped for use, consumers can treat Gold as the trusted version of the truth for analytics. Do this well: Model for analytics. If using a star schema, define the right grain for facts and use clean dimension tables. Use aggregations to reduce data volume and make common queries fast. By Gold, sensitive data should be removed, masked, or protected with row-level or column-level security. Document lineage, especially how Gold fields are derived from Silver. Decide a refresh strategy. Gold is usually rebuilt or incrementally updated on a schedule. Encapsulate repeatable refresh logic in stored procedures when that makes the pipeline easier to operate. The layers at a glance Use this as a quick health check for your Fabric DW medallion design. Layer Its one job In Fabric DW Watch out for Bronze Preserve raw source data Staging tables, COPY INTO, metadata columns Cleaning too early; tiny files Silver Clean, validate, and conform CTAS, INSERT...SELECT, MERGE, quality gates Non-repeatable transformations Gold Serve trusted analytics Facts, dimensions, marts, aggregations Business logic leaking in late Takeaway Data flows Bronze (raw) → Silver (cleaned) → Gold (curated) — and the discipline is in the arrows. If you can point at any table and say which single job it serves, your medallion architecture is healthy. When something breaks in Gold, a clean Bronze layer and a repeatable Silver layer let you recompute from scratch instead of reverse-engineering business logic from reports. This post is part of our Medallion Architecture on Fabric Data Warehouse series: Choosing your medallion pattern in Fabric Data Warehouse Building the Bronze → Silver → Gold layers Fabric DW best practices for medallion architectures Securing and governing your layers Performance tuning your medallion pipeline In the next post, we'll explore the Fabric DW-specific best practices that keep all three layers fast, reliable, and governable.
Understanding Warehouse Consumption with Capacity Metrics and Query Insights
When reviewing warehouse usage, customers often start with the same question: which workloads contributed to the consumption I’m seeing in the Fabric Capacity Metrics app? With the updated consumption accrual model for Fabric Data Warehouse and the SQL analytics endpoint, the best way to answer that question is to use Capacity Metrics and Query Insights together. Capacity Metrics shows warehouse-level CU consumption over time based on the virtual nodes allocated to the warehouse, while Query Insights helps you understand the queries, users, and workloads active during that same period. This blog walks through a practical approach for applying query-level weighted attribution from warehouse-level consumption data. Start with the Capacity Metrics App The Fabric Capacity Metrics app remains the source for getting the Capacity Units (CUs) consumed by the warehouse. It shows how many CUs were consumed by a warehouse during a given time range. Use the Metrics app to identify: Which warehouses are consuming the most CUs When consumption spikes occurred Which workloads are contributing most to capacity usage How much Warehouse consumption was attributed to user initiated workloads vs system initiated workloads. The following example shows the Capacity Metrics app timepoint detail view used to identify a period of warehouse consumption for further analysis. Capacity Metrics identifies when consumption occurred, but it doesn't explain which specific queries contributed to that consumption. To answer that question, use Query Insights. Drill into Query Insights After identifying a time window of interest, Query Insights can help explain what drove that consumption. The queryinsights.exec_requests_history view captures the allocated CPU time, or vCore seconds, for each query, enabling you to analyze which users, workloads, and queries are consuming compute resources. DECLARE start_Time DATETIME2(0) = '2026-08-10 8:00:00' ,@End_Time DATETIME2(0) = '2026-08-10 9:00:00' SELECT [database_name], sql_pool_name, distributed_statement_id, login_name, allocated_cpu_time_ms / 1000.0 AS vcore_seconds FROM queryinsights.exec_requests_history WHERE start_time < @End_Time AND end_time > start_Time Applying Weighted Consumption by Query Now that we have data for the workloads that were running for the period of interest, we can combine these two datasets to get the weighted consumption of each workload's contribution to overall warehouse consumption. For a given period: Capture the total CU consumption from the Capacity Metrics app. Sum the vCore seconds of all the queries from Query Insights. Allocate CU consumption proportionally based on each query’s percentage of total vCore seconds. For example, if a query represents 25% of the total vCore seconds consumed during a period, you can attribute approximately 25% of the warehouse CU consumption to that query. Because Capacity Metrics reports warehouse-level consumption and Query Insights reports query-level activity, the resulting attribution is a weighed attribution of warehouse consumption. You can further analyze the data by attributing cost to certain SQL pool usage or by users. Practical Scenarios This approach can help answer questions such as: Which users contributed most to warehouse consumption during a peak period? Which workloads should be prioritized for optimization? Which SQL pools or warehouses are driving the highest capacity usage? How did a recent deployment or workload pattern change affect consumption? Bringing It Together Think of the two tools as answering different questions: Capacity Metrics: How much consumption occurred over time Query Insights: What was running during that time By using Capacity Metrics and Query Insights together, you can move from simply seeing warehouse consumption to understanding what drove it. While attribution remains weighted attribution of the workloads executed, this approach helps identify expensive workloads, prioritize optimization efforts, and make more informed capacity decisions. Learn More To learn more about monitoring and understanding warehouse consumption, see: How to Observe Fabric Data Warehouse utilization trends Billing and utilization reporting in Fabric Data Warehouse Fabric OperationsFabric July 2026 Feature Summary
Welcome to the July 2026 Fabric update! This month brings new capabilities across the Fabric experience, from improved deployment and governance experiences to expanded Spark, Eventstream, and Real-Time Intelligence functionality. Whether you're building data pipelines, managing analytics workloads, or monitoring real-time operations, these updates help you work more efficiently and get more value from your data. _______________________________ Events and Announcements Get Fabric certified for FREE This is your chance to take the DP-600 (Fabric Analytics Engineer) or DP-700 (Fabric Data Engineer) certification exams for free. As part of Data Days, we have over 100 live sessions, more than 5 contests and challenges, and dozens of study groups and learning opportunities. And free Fabric exam vouchers. Available now through August 10, 2026. Request your voucher. Join us for FABCON and SQLCON in Barcelona, September 28 – October 1, 2026 Explore what’s possible with Microsoft Fabric and get up to speed on the latest in SQL, analytics, and AI. From 130 sessions and 4 keynotes to workshops, the expo, community spaces, and the Power BI DataViz World Championships, this is where the data community comes together. Learn directly from Microsoft and community experts shaping the future of Fabric and SQL. Register now and save €200 with code FABCMTY200. Fabric Platform Fabric-CICD tool v1.2.0 – new bulk publish mode (Preview) The June release of the fabric-cicd Python library, v1.2.0 introduces bulk publish mode. It lets fabric-cicd publish multiple items in a single API call using the Fabric bulk import API instead of publishing each item individually through a separate API call. This can make deployments more efficient. Why this matters Because dependencies are managed by the API during publication, bpublishinglish reduces the rigidity of item-type-based staging and better supports cross-item dependencies when logical ID references are used. It can reduce the number of item-specific parameter values you need to configure. For unsupported scenarios, fabric-cicd automatically falls back to the standard publishing flow, so you can try bulk publishing without manually switching deployment paths. To learn more, refer to fabric-cicd bulk option documentation and the v1.2.0 release notes. Change Git branch with at least the contributor role (Preview) Fabric Git integration now let’s any workspace member with at least the Contributor role switch the workspace's connected Git branch. Previously, this action required the workspace Admin role, which forced developers to either be over-permissioned or wait on an admin every time they moved between branches. Why this matters Developers no longer need Admin rights to switch branches. Removes a key blocker in the branch-out to existing workspace flow. Keeps developers as Contributors, aligned with least privilege governance. Fewer hand-offs to admins mean faster time to code. You can find this new capability under the Git integration settings. To learn more about Fabric Git integration new setting, refer to the Allow Contributors and Members to switch branches documentation. We refreshed the Fabric CI/CD documentation this month to make it easier to get started and to follow best practices: New CI/CD intro page — a reworked Introduction to CI/CD in Microsoft Fabric that walks through the platform layer by layer with a new enterprise reference architecture. New best practices guide — Understand the best practices for Fabric CI/CD brings together practical guidance for structuring workspaces, branching, and promoting content safely across environments. Auto-bind for Git integration — new guidance on cross-workspace dependency binding, covering how item dependencies automatically rebind when you branch out or update from Git, and which dependency types are supported. Actionable recommended actions for data owners in the OneLake catalog The Govern tab in the OneLake catalog gives data owners a health view of their data estate, along with recommended actions to improve it, such as increasing sensitivity label coverage, removing items that are no longer in use, handle failed refresh, and more. Until now, these recommendations have told data owners what to improve, but not which items were affected, leaving them to track the relevant entities down manually before they could act. That gap turned a clear recommendation into a manual investigation, and the action often stalled before it started. This release closes that gap. When you open a recommended action, you now see the specific items behind the recommendation, with everything you need to act on them in one place, and a direct path to open item details and resolve the issue at the source. Selecting a recommended action now opens a dedicated view that includes: A breakdown of the affected items — a visual summary of how many of your items are impacted. Why it matters — a short explanation of the governance impact. How to fix it — clear, step-by-step instructions for resolving the recommendation. Newly added A complete list of affected items — displayed in a table with key details, including Name, Last Refreshed, Owner, Location, Endorsement, and Sensitivity, making it easy to review and prioritize actions. Filter and search — narrow the list by keyword or filters to focus on the items you want to handle first. Open item details — jump straight to any affected item to make the change, instead of searching for it across your workspaces. The same enriched experience applies across the recommended actions in OneLake catalog Govern, helping you act on each one without leaving the catalog: Increase Sensitivity label coverage — find and label items that create potential security risks while unlabeled. Remove unused items — review items that weren't accessed or refreshed recently to keep your estate organized and reduce costs. Investigate items that failed to refresh — see which items failed to refresh so your data stays current and reliable. Add descriptions to your endorsed items — surface endorsed items without a description so people can better understand and use them. Apply relevant tags to your items — make items more discoverable by tagging the ones that are missing tags. Why it matters Governance only improves when recommendations turn into action. By bringing the affected items and a direct way to reach them into every recommended action, OneLake catalog Govern removes the guesswork from following through. Data owners can move from understanding a recommendation to resolving it in just a few steps, keeping their data estate secure, organized, discoverable, and trustworthy with far less effort. Learn more about the OneLake catalog Govern tab recommended actions. Data Engineering Microsoft Fabric Runtime 2.0 (Preview) Based on feedback received directly from our customers and partners, we have upgraded Fabric Runtime 2.0 to the latest and compatible stack: Apache Spark 4.1 Delta Lake 4.2 Python 3.13 These upgrades bring access to the newest enhancements, performance improvements, and ecosystem innovations, while also providing a longer support window for enterprise customers to build large-scale analytics and AI workloads on Fabric. With Runtime 2.0, customers can take advantage of: Latest Spark and Delta Lake capabilities. Improved compatibility across the modern data ecosystem. Better developer productivity and runtime performance. Future-ready platform investments aligned with long-term supportability. Learn more about Fabric Runtime 2.0. Fabric Runtime Release Channels (Preview) Fabric Runtime Release Channels provide a structured and transparent way for customers to test upcoming runtime changes before they become the default. This feature helps organizations validate their production workloads early with these new changes in early access, avoid unexpected disruptions, and gain better control over Spark runtime upgrades. Instead of receiving silent updates that might break your production workloads, you can opt in to an early access release channel, test your workloads in a development or staging environment, and confirm compatibility before the update becomes default. How release channels work Each Spark runtime has at least two public release channels: Default channel — This production-grade channel runs the default version of the runtime. All users automatically use this channel unless they opt in to early access. Early access channel — This production-grade channel includes upcoming updates and library changes that are scheduled to become the next default channel. You can opt in to test your workloads against upcoming changes. Once the designated validation window ends, the early access release channel automatically gets promoted to become the new default, and a fresh early access channel is introduced with another set of new changes — continuing the cycle. This model gives you a predictable testing window before changes become default for everyone. Why release channels matter Spark runtime updates can include library upgrades, security patches, dependency changes, or even operating system upgrades. While all updates pass internal quality checks before release, those checks can't capture all customer-specific variations and use cases. Early access channels let you identify potential issues early and work with Microsoft by creating a support ticket to address them before updates affect your production environment. Benefit Description ✔ Predictable updates Customers know exactly when a new runtime becomes available and have time to validate against it. ✔ Reduced risk Testing workloads on early access ensures compatibility before changes reach production. ✔ Better visibility Customers can easily tell which runtime version they're running, reference release notes, and verify upgrade timing. ✔ Improved quality and security You receive well-tested builds with security patches applied faster, giving you confidence in runtime stability. Learn more about Fabric Runtime Release Channels: Fabric Runtime Release Channels. Spark Diagnostic Emitter: Spark 4.1 Runtime Support and New Log Ingestion API The Fabric Apache Spark Diagnostic Emitter is now supported on the Fabric Runtime with Apache Spark 4.1. Customers can collect driver logs, executor logs, Spark event logs, and metrics from workloads running on the latest runtime and route them to Azure Event Hubs, Azure Blob Storage, or Azure Log Analytics — using the same spark.synapse.diagnostic.emitter.* configuration model, so existing emitter setups carry forward as customers upgrade. The emitter also now supports the Azure Monitor Log Ingestion API for sending diagnostics to Log Analytics, available on both Spark 3.5 and Spark 4.1 runtimes. The new AzureLogIngestion emitter type replaces the legacy HTTP Data Collector API path, providing a structured ingestion model with DCR/DCE-based authentication, schema definition, and routing into custom Log Analytics tables. Customers currently on the legacy AzureLogAnalytics type are encouraged to migrate — migration involves creating Data Collection Rule and Data Collection Endpoint resources and updating the Spark properties in the Fabric Environment. To learn more, refer to the Collect logs and metrics with Azure Log Analytics for migration guidance and Spark Diagnostic Emitter documentation. The Fabric Spark Operations Skill — AI-Assisted Spark Diagnostics, Open Source (Preview) The Fabric Spark Operations Skill (spark-operations-cli) is now available in the open-source Skills for Fabric library on GitHub (microsoft/skills-for-fabric). The skill brings AI-assisted, read-only diagnostics to Spark workloads in Fabric — troubleshoot failed notebooks and Spark jobs, stuck Livy sessions, and performance bottlenecks (OOM, shuffle, skew) in plain English from GitHub Copilot CLI, Claude Code, VS Code, Cursor, or other compatible AI tools. It returns severity-ranked findings with root cause analysis and fix recommendations, and includes an automated diagnostic workflow spanning job triage, log mining, Spark Advisor findings, and mitigations. Setup takes minutes: install the skill, run az login, and ask, "Why did my notebook fail last night?" Get started with Skills for Fabric in GitHub. Faster Python UDFs, Scala UDFs, and complex data types in the native execution engine (Generally Available) The native execution engine in Microsoft Fabric, which is generally available, now accelerates Python user-defined functions (UDFs), Scala UDFs, and complex data types such as arrays, maps, and structs. You get faster Spark processing for expressive code without changing your existing notebooks or jobs. Python UDFs have historically carried serialization overhead as data moves between the JVM and Python worker processes. The native execution engine optimizes that data path and keeps vectorized processing intact, so scalar and Pandas (vectorized) UDFs run faster automatically. Scala UDFs and queries that work with nested, complex data types benefit from the same native acceleration. Why this matters Faster UDF execution with no code changes. Vectorized Pandas UDFs see the largest gains. Complex types (arrays, maps, structs) run natively. Existing notebooks and jobs benefit automatically. To use Efficient Scaledown, enable the native execution engine on your Fabric Spark pool or environment. Existing Python and Scala UDFs and queries over complex types are accelerated without any code changes. To learn more, refer to the Python UDFs, Scala UDFs, and complex data types in the native execution engine documentation. Efficient Scaledown with the remote shuffle manager (Preview) Efficient Scaledown decouples Spark shuffle data from executor lifetime in Microsoft Fabric. Instead of pinning shuffle output to local executor disks, Fabric Spark routes large shuffles to Azure Blob Storage and migrates blocks off executors before they're released. Clusters scale down faster, compute costs drop, and jobs become more resilient — with no changes to your queries, notebooks, or pipelines. The feature combines four cooperating capabilities: the Remote Shuffle Manager writes and reads shuffle data to Azure Blob Storage; Shuffle Migration moves blocks off an executor before decommissioning instead of dropping them; a Decision Layer routes small shuffles to local disk and large shuffles to remote storage per stage; and AQE Shuffle Write lets Adaptive Query Execution shape partitioning the first time. Why this matters Clusters scale down faster after demand drops. Lower compute cost from quicker executor release. More resilient jobs with fewer stage retries. No changes to queries, notebooks, or pipelines. To use it, enable the native execution engine and run on Runtime 1.3 (Apache Spark 3.5) or later; autoscale is recommended. Remote Shuffle Manager spark.conf.set("spark.remote.shuffle.enabled", "true") Decision Layer — per-stage routing of local vs. remote shuffle spark.conf.set("spark.sql.rsm.decisionlayer.enabled.level", "stage") AQE participates in shuffle write spark.conf.set("spark.sql.adaptive.shuffleWrite.enabled", "true") Shuffle Migration on executor decommission spark.conf.set("spark.storage.decommission.shuffleBlocks.enabled", "true") spark.conf.set("spark.storage.decommission.shuffleBlocks.cleanup", "true") spark.conf.set("spark.storage.decommission.shuffleBlocks.migrateToFallbackStorage", "true") spark.conf.set("spark.storage.decommission.fallbackStorage.cleanUp", "true") To learn more, refer to the Efficient Scaledown and remote shuffle manager in Microsoft Fabric documentation. Customer-managed key encryption for Spark shuffle data on disk (Generally Available) Microsoft Fabric Spark now generally supports customer-managed keys for Spark jobs through a disk encryption set. Shuffle data written to cluster disks during a Spark job is encrypted with a key you supply and control, giving you ownership of the encryption material that protects intermediate data at rest. With a disk encryption set configured, the cluster disks that hold Spark shuffle data are encrypted using your customer-managed key rather than a platform-managed key. You manage the key lifecycle — including rotation and access — in your own key vault, so encryption of intermediate Spark data follows your organization's key management policies. Why this matters You control the key protecting shuffle data. Intermediate Spark data on disk is encrypted at rest. Key lifecycle and rotation stay in your control. Encryption aligns with your key management policies. To use it, configure a disk encryption set backed by your customer-managed key and associate it with your Fabric Spark configuration. Spark jobs then encrypt shuffle data on cluster disks with your key. To learn more, refer to the Customer-managed key encryption for Fabric Spark documentation. Query your data instantly with the Lakehouse Query Explorer (Generally Available) The Lakehouse Query Explorer is a new, fully integrated query editor built directly into the Lakehouse experience in Microsoft Fabric. You can now write and run Spark SQL queries right where your data lives — no need to switch to a SQL endpoint or spin up a notebook for quick exploration. Whether you’re validating datasets, iterating logic, or exploring patterns, Query Explorer keeps you in flow. orer with the new integrated Query Explorer. Key capabilities Fast, lightweight Spark execution powered by the Lakehouse Livy endpoint. IntelliSense + rich editing experience for faster query authoring. Query across schemas and lakehouses in a single tab. Built-in results grid + inline charts to explore results instantly. Multiple dynamic tabs to analyze different slices of data side-by-side. From quick lookups to multi-table exploration, Query Explorer makes working with Lakehouse data faster and more intuitive—right from the explorer. Learn more and get started with the Lakehouse Query Explorer documentation. Analytics and Insights for Materialized lake views (Generally Available) Analytics and Insights bring continuous refresh intelligence to your lakehouse — two new tabs beside Recent run(s) that shift you from reacting to individual failures to staying ahead of performance drift, rising costs, and silent inefficiencies across your entire materialized lake view estate. The Recent run(s) page tells you what happened in a single execution, including which views succeeded, which failed, and how long the job took. That's valuable when something breaks, but it doesn't answer the questions that matter most for day-to-day operations. Are my durations stable or slowly climbing? Is a new error class appearing and spreading across views? Are my schedules still aligned with how often upstream data actually lands? Am I paying for full refreshes on views that could run incrementally? These are the questions that separate a well-tuned deployment from one that quietly accumulates cost and risk until something finally breaks loudly enough to notice. The Recent run(s) page answers all of them. The Analytics tab transforms your run history into trend lines, distributions, and comparisons you can read at a glance: duration trajectories, success-rate shifts, error-class frequency over time. The Insights tab goes further by watching those same patterns the way a seasoned reliability engineer would, recognizing the signatures behind slow, failing, or wasteful runs, and providing a prioritized list of worthwhile changes. Each recommendation names the affected view, explains why it's flagged, and estimates the payoff in runtime savings, so you can act in seconds rather than investigate for hours. Together, they move refresh management from a reactive, break-fix posture to a proactive, continuously improving one — keeping your materialized lake views fast, healthy, and cost-efficient as your estate grows. To learn more, refer to analytics charts, insight categories in Materialized Lake Views. Introducing Event-Driven Refresh for Materialized Lake Views (Preview) Event-driven refresh brings responsive refresh intelligence to your lakehouse — a new scheduling mode alongside time-based schedules that shifts you from refreshing on the clock to refreshing the moment your data is ready, so your materialized lake views reflect reality instead of an arbitrary calendar. Time-based schedules tell your views when to run: every hour, every morning, every night. That's dependable when upstream data lands like clockwork, but it doesn't answer the questions that matter most for day-to-day operations. Did the ingestion pipeline that feeds this view finish before I refreshed it? Am I recomputing gold-layer views on a fixed cadence while the source data only changes twice a day? Am I paying for refreshes that run before new data has even arrived — or worse, serving stale results because the next scheduled slot is still hours away? These are the questions that separate a refresh strategy tuned to your data from one that quietly burns compute on empty runs and lags the moments that matter. Event-driven refresh answers all of them. You bind a view or a lineage sub-chain to the events that should drive it, and we support two types of events: OneLake events — all file and folder events are supported, so a refresh can fire the moment data lands in OneLake (file or folder creation, update, and more). Job events — Pipeline and Notebook events are supported, so a refresh can fire when the ingestion job that feeds your views completes. The moment an event fires, Fabric resolves the dependency chain and refreshes exactly the views that depend on it. Each trigger names the source event, scopes precisely to the affected lineage, and can react to success or failure, so a stalled upstream job never silently cascades into stale downstream reports. Together with multi-schedule support, event-driven refresh moves refresh management from a fixed-clock, guess-the-cadence posture to a responsive, data-driven one — keeping your materialized lake views fresh the instant new data arrives, and idle when it hasn't, as your estate grows. Learn more and get started with the Schedule a Materialized Lake View Refresh documentation. Data Science AI functions: new models, no package dependency, better usage stats (Generally Available) Fabric AI Functions now use gpt-5-mini as the default model, with “low” reasoning enabled. This powers AI Functions across pandas, PySpark, Data Warehouse, and Dataflows Gen2. For more sophisticated transformations, users may configure gpt-5.1 or tune the reasoning_effort parameter for additional compute and higher-quality results. The gpt-4.1 model has been retired. Pipelines pinned to gpt-4.1 have migrated to gpt-5.1, and those pinned to gpt-4.1-mini migrated to gpt-5-mini. We’ve also simplified PySpark AI Function chaining. The PySpark .ai interface now stays bound to the result schema, so chains like summarize → classify no longer require intermediate DataFrames. In addition, PySpark now supports df.ai.stats for detailed token usage after any AI function call, including reasoning token breakdowns. For pandas, AI Functions no longer require the openai-python package. Capacity-limited rows are surfaced as CapacityExceededResult, enabling clean retries via aifunc.split_results. To learn more, refer to the AI Functions documentation. Data Warehouse Lakehouse table health check (Generally Available) Lakehouse Table Health Check gives you a simple, T-SQL–based way to validate whether your Lakehouse tables are optimized for the SQL analytics endpoint. A single stored procedure surfaces common layout issues, such as small files and fragmentation, offering the insights you need to determine if your tables need to be optimized. You can integrate health checks into pipelines and operational workflows, enabling proactive, at-scale optimization instead of reactive troubleshooting. -- Run a health check on a Lakehouse table from the SQL analytics endpoint EXEC sp_get_table_health_metrics 'dbo.FactSales'; aluate table health. To learn more about sp_get_table_health_metrics and how to integrate it into your Pipelines to optimize your table only if anomalies are detected, refer to sys.sp_get_table_health_metrics (Transact-SQL). Scalar User‑Defined Functions - Procedural computation for analytical SQL (Preview) Scalar User-Defined Functions now support procedural computation — including loops, multiple return paths, and rich IF/THEN/ELSE branching — running natively within the warehouse engine. Computation-based Scalar UDFs are designed for analytical query shapes, integrating naturally with CTEs, GROUP BY, HAVING, and ORDER BY, and executing efficiently at data warehouse scale. Defining business rules once, in SQL, and reuse them across queries, reports, and pipelines. To learn more, refer to the Create Function documentation. Usage-based resource estimations (Generally Available) The Query Optimizer uses learned resource estimation to correct underestimation in T-SQL query plans. It saves actual cardinalities from past executions and automatically adjusts row count estimates in subsequent runs. With this release, the optimizer will also begin correcting overestimations — closing the loop in accurate cardinalities, leading to more efficient resource requests and improved concurrency. Real-Time Intelligence Improved tile error experience in Real-Time Dashboard (Generally Available) We improved the way tile errors appear in Real-Time Dashboards to make them clearer, calmer, and easier to act on. Instead of showing a disruptive red error state, tiles now use a neutral grey error state with a short category header, such as Syntax error, Semantic error, Data source issue, Network error, or Something went wrong. Users can select Details directly from the tile to open a popover with the full engine error message, shown exactly as received. This keeps the dashboard readable while still making the technical details easy to access, copy, and share when needed. This update helps users understand what went wrong faster, while ensuring that one failed tile does not block or visually overwhelm the rest of the dashboard. To learn more, refer to the Troubleshoot Real-Time Dashboard Tile Errors documentation. Folders in Eventhouse tree (Generally Available) Folder support in the Eventhouse tree is now available through the UI. Previously, folders could only be created and managed via code. With this update, you can now organize your Eventhouse directly from the tree, making it easier to manage assets at scale. You can group tables, shortcuts, materialized views, functions, and data streams into a structured hierarchy, improving navigation and reducing clutter. Key capabilities Create, rename, and delete folders from the UI. Organize assets into folders. Move items via the context menu (⋯ → Move to). Create folders inline while moving items. This allows for a simplified and more intuitive way of managing growing Eventhouses. To learn more, refer to the Manage and monitor a KQL database table documentation. Eventstream connector private network support (Generally Available) Data is a critical asset for organizations, and access to real-time data is increasingly essential. However, many high-value data sources reside in private network environments — cloud virtual networks or on-premises infrastructure — particularly in highly regulated industries such as banking, finance, and telecommunications, where strict security and compliance requirements are mandatory. Eventstream's private network support establishes a secure, managed bridge using your Azure virtual network and VNet injection, allowing Eventstream streaming connectors to run inside your virtual network and reach private sources without opening them to the public internet. Whether your data sources reside in on-premises networks, private networks on third-party cloud services, or private networks on Azure, you can connect the bridge Azure virtual network to your source's private network using suitable connectivity options — such as VPN or ExpressRoute for on-premises environments, and private endpoints or network peering for Azure-based sources — enabling Eventstream connectors to securely ingest real-time data from these protected environments into Fabric. The solution leverages a new concept — Streaming virtual network data gateways — which abstracts the bridge Azure virtual network and subnet resource within Fabric. By creating a connector with a streaming virtual network data gateway associating it with your connection, the Eventstream connector is provisioned within your virtual network, ensuring secure communication with your private data sources. Once real-time data from your private network source is securely brought into Fabric Eventstream, you can fully leverage the comprehensive analytics tools in Fabric Real-Time Intelligence to power your real-time scenarios with enterprise-grade security. To learn more about configuration and advanced scenarios, refer to the Eventstream private network streaming guide. Azure Event Hubs source in Eventstream now supports workspace identity authentication (Preview) Currently, when configuring an Azure Event Hubs source in Eventstream, the only supported authentication method is Shared Access Key — a connection string containing static credentials. While simple to set up, shared access keys present several security risks in production environments: they have unlimited lifetime unless manually rotated, provide no per-user or per-application identity, and if accidentally leaked through source code, configuration files, or third-party sharing, grant full access to anyone who obtains them. Revoking a compromised key requires regeneration, which disrupts all services depending on it — with no straightforward way to audit which clients used the key. To address these challenges, we're introducing Workspace Identity as a new authentication option for the Azure Event Hubs source connector (Extended features) in Eventstream (Preview). A Fabric workspace identity is an automatically managed service principal associated with your workspace. Fabric manages the credentials entirely — there are no secrets to store, rotate, or risk leaking. It integrates with Microsoft Entra ID, providing identity-based access with full audit trails, fine-grained role-based access control, and automatic credential lifecycle management. To use workspace identity authentication with your Azure Event Hubs source in Eventstream: Navigate to your workspace settings and create a workspace identity on the Workspace identity tab. In your Azure Event Hub namespace, assign the appropriate role (e.g., Azure Event Hubs Data Receiver) to the workspace identity's service principal. When adding an Azure Event Hubs source in Eventstream, select Workspace Identity as the authentication method — no connection string or key is needed. Eventstream will automatically obtain tokens using the workspace identity to securely connect to your Event Hub, eliminating credential management overhead while strengthening your security posture. To learn more about the configuration, refer to the Azure Event Hubs source extended connector configuration documentation. Custom CA and mTLS support in Eventstream streaming connectors (Generally Available) Fabric Eventstream under Real-Time Intelligence provides various streaming connectors, enabling the integration of real-time data from popular sources into Fabric. When the Eventstream connector client establishes a connection with sources, it is required to implement TLS or mTLS encryption to fulfil the necessary security standards. Many organizations use certificates issued by private or internal Certificate Authorities or require mutual TLS (mTLS) authentication where both the client and server verify each other's identity before transmitting data. Without custom CA and mTLS support, Eventstream connectors cannot connect to these secured source systems. The Custom CA and mTLS support feature, is now generally available for MQTT, Apache Kafka, AWS MSK, and Confluent Cloud for Apache Kafka source connectors. Customers can specify their custom CA and client certificates managed in their own Azure Key Vault when configuring their source in Eventstream. Once specified, Eventstream connector will fetch the certificates from the customer's Azure Key Vault and use them to establish a mutually authenticated, encrypted connection — enabling secure, compliant real-time data ingestion across all supported streaming sources. To learn more about the configuration, refer to the Eventstream sources overview page and choose the corresponding source. Introducing the Oracle CDC connector for Eventstream (Preview) Eventstream now introduces the Oracle Database Change Data Capture (CDC) connector, enabling you to stream database change events directly from any Oracle Database — whether running in the cloud or on-premises — into Eventstream for real-time processing and analytics. Many organizations run important operational workloads on Oracle Database and need to react to changes as they happen. With the Oracle CDC connector, you can continuously capture change events from Oracle Database and bring them into Fabric without building custom polling applications or managing separate integration services. With the Oracle CDC connector, you can: Capture and stream databases changes from Oracle Database into Fabric in real time. Connect to Oracle databases running either on-premises or in the cloud. Process incoming change events using Eventstream transformations. Route processed change events to supported destinations such as Eventhouse, Lakehouse, Activator, or custom endpoints. This capability helps you build real-time analytics and event-driven applications from Oracle data. For example, you can route transaction changes to an Eventhouse Kusto table for operational analysis, send selected events to Activator for alerting, or combine Oracle change events with other streaming sources in the same eventstream. To learn more about Eventstream Oracle CDC connector, refer to Add Oracle Database CDC source to an eventstream (preview). Eventhouse update policies now support referencing accelerated shortcuts in update policies (Generally Available) Eventhouse update policies now support accelerated shortcuts in update policy queries, enabling ingestion-time enrichment scenarios. Use this for dimension lookups, such as enriching ingested fact events with customer, device, or product attributes stored in OneLake shortcut data. The shortcut-backed external table must have Query Acceleration Policy enabled, and Hot must cover all data. For update policy scenarios, set: .alter external table DimCustomer policy query_acceleration '{"IsEnabled":true,"Hot":"36500.00:00:00"}' Then join to it from the update policy query: .alter table EnrichedEvents policy update '[{ "IsEnabled":true, "Source":"RawEvents", "Query":"RawEvents | lookup kind=leftouter (external_table(''DimCustomer'')) on CustomerId","IsTransactional":true, "PropagateIngestionProperties":false }]' Processing uses the authorization context captured in the system-populated OwnerPrincipalDetails property: the user who creates or alters the update policy must have access to the shortcut data. This enables ingestion-time enrichment with governed shortcut data without separate orchestration. Shortcut Tables in Eventhouse Now Automatically Synchronize Schema Changes To help maintain consistency between source data and shortcut tables, the default schema synchronization behavior for Eventhouse shortcut tables is changing. Previously, schema changes made to the source table were not automatically propagated to the shortcut table unless schema synchronization was explicitly enabled. As a result, source and shortcut schemas could diverge over time. With this update, all new and existing shortcut tables in Eventhouse automatically synchronize schema changes from their source table by default, including: Adding new columns Changing column data types Renaming columns Deleting columns Automatic schema synchronization helps ensure that shortcut tables remain aligned with the source schema, preserving the latest business context and reducing manual maintenance. Customers who prefer to disable automatic schema synchronization can continue to control this behavior using the existing KQL management command: .create-or-alter external table ExternalTable kind=delta ( h@'https://storageaccount.blob.core.windows.net/container1;secretKey' ) with (AutoUpdateSchema=false) Note: Because schema changes are now automatically propagated, queries, dashboards, and downstream workloads may require updates if they reference columns that are renamed, removed, or otherwise modified in the source table. Investigator Insights in Operations Agent (Preview) When an anomaly is detected, understanding what caused it is often the hardest part. Investigator insights are designed to make that easier by analyzing the surrounding data and surfacing relevant context. When the operations agent detects that a rule has been met, there is an option to run an investigation in the background to identify correlated signals and patterns. Instead of manually digging through telemetry, you get a guided view into what changed, what stood out, and what may have contributed to the issue. This helps you move more quickly from detection to understanding. You can access these insights directly from Teams. Open the agent’s message and select Investigate further to generate a detailed analysis. The investigation provides a structured view of what happened: Investigation scope shows which tables were analyzed, whether from a single source or across related datasets. Key observations highlight the most important findings, including actual values, deviations from baseline, and notable trends or outliers. Pattern analysis surfaces meaningful changes around the time of the anomaly, such as dimensions with significant shifts, and clearly call out when no strong patterns are identified. Together, these insights help you quickly understand not just that something went wrong, but why it happened. To learn more, refer to the Operations Agent Actions documentation. Anomaly Detector Configurations Pane As you build on top of your data, it is often just as important to understand what already exists as it is to create something new. This update makes it easier to discover and build existing anomaly detection configurations. You can now view all anomaly detection configurations that have already been created for a given data source in one place. This lightweight experience gives you quick visibility into how anomaly detection is currently set up, helping you avoid duplicate work and better understand how others are using the data. From this view, you can explore existing configurations or create a new one if your use case is not yet covered. This makes it simple to extend existing setups or start fresh when needed, all without leaving the context of your data source. By making configurations easier to discover and reuse, this experience helps streamline workflows and ensures you can move quickly from exploration to action. To learn more, refer to the Anomaly Detection in Real-Time Intelligence documentation. Ingestion time stamp in Anomaly Detector (Preview) Working with anomaly detection often assumes your data already includes a clean, reliable timestamp. With this update, you can now use the system-generated ingestion time as the timestamp for anomaly detection. This means that even if your dataset does not include a dedicated timestamp column, you can still run analysis without needing to modify or preprocess your data. This is especially helpful for scenarios where events are ingested in real time or where timestamps are missing, inconsistent, or not trustworthy. Instead of blocking data preparation, you can rely on ingestion time to move forward with detection and start identifying meaningful patterns right away. By reducing setup requirements and removing a common dependency on source data quality, this capability makes it easier to apply anomaly detection across a wider range of use cases. To learn more, refer to the Anomaly Detection in Real-Time Intelligence documentation. Configuring Anomaly Detection without a Group by Column (Preview) With this update, Anomaly Detection now supports scenarios where you choose not to use a group by column. This provides an additional configuration option for datasets that already represent a single stream of data, allowing you to apply anomaly detection directly to the metric without first identifying a grouping dimension. This added flexibility helps the configuration experience better align with how your data is structured. Whether you are monitoring a single device, tracking a specific service, or analyzing a focused dataset, you can now create anomaly detectors without requiring a group-by column. At the same time, grouping remains available for scenarios where you want to monitor and compare multiple entities within the same dataset. To learn more, refer to the Anomaly Detection in Real-Time Intelligence documentation. Introducing Fabric Maps Tilesets: High-Performance Visualization for Large Geospatial Datasets (Generally Available) Have location-based data sitting in OneLake but no simple way to see it come alive on a map? With Fabric Maps Tilesets, you can now turn that data into fast, interactive map experiences directly inside Microsoft Fabric. The Tileset Builder lets you create map-ready PMTiles from OneLake data without custom code, manual exports, or separate geospatial infrastructure making it easier for teams to explore large geospatial datasets, uncover patterns, and bring location intelligence into the analytics workflows they already use. Organizations can rely on Microsoft Fabric to manage operational data, analytics workflows, and business reporting. When that data includes locations, routes, assets, boundaries, or events with a location context, teams need a simple way to visualize it on a map. Traditionally, this required separate geospatial pipelines, custom polling services, or manual exports. Leveraging a Fabric Map removes complexity by allowing organizations to create and refresh map-ready tilesets from data already stored in OneLake. Try creating your own Tileset using the following steps: Connect to a lakehouse and select source files Configure tileset metadata Configure layer settings Tileset schedule (Preview) Review and create tileset What is a Tileset? Tilesets are map-optimized representations of geospatial data. Instead of trying to load a large geospatial data file all at once, data is divided into small tiles that are loaded and rendered as needed while users zoom and pan across the map. This makes tilesets especially useful for large datasets, such as infrastructure networks, delivery routes, asset locations, service areas, or operational events. Fabric Maps Tileset Builder Capabilities Build map-ready tilesets directly from OneLake data. Visualize large geospatial datasets with high performance. Enable smooth, interactive map experiences at scale. Keep map content synchronized with source data. Eliminate manual exports and external geospatial processing workflows. Integrate native geospatial visualization into existing Fabric data workflows. Common scenarios Tilesets help teams turn large location-based datasets into fast, interactive map experiences. Utility and energy companies can visualize nationwide power line grid systems as a single dataset to monitor field assets and service coverage. Supply chain and logistics teams can explore daily routes, regional boundaries, and operational areas. Retailers can analyze store territories, expansion opportunities, and new developments. Because the data stays connected to Fabric, these map experiences become part of the broader analytics workflow — not a separate geospatial process. To get started, refer to How to create tilesets. Cross-domain intelligence with Azure Monitor data in Microsoft Fabric (Preview) Have you ever detected an issue in your systems but struggled to understand what it meant for the business? As systems grow more complex, this gap becomes harder to bridge. Incidents no longer affect just systems; they affect customers, revenue, and operations in real time. Azure Monitor Logs mirroring into Microsoft Fabric helps close that gap. In just a few steps, telemetry from Log Analytics workspaces becomes available in OneLake alongside business and operational data — without duplication and with near real-time availability. This creates the foundation for Cross-domain insights and actions: Bring observability, operational, and business data together in Eventhouse for real-time analysis. The same unified data can be used by Real-Time Dashboards for investigation and by operations agents to recommend and drive actions informed by both business and observability context. For example, an operations team can identify that a check-in kiosk outage is impacting high-value customers and act before customer impact grows. Advanced Fabric analytics Apply tools like Spark and Power BI for long-term analysis, machine learning, and a wide range of analytical scenarios. For example, an operations team can create a Power BI report showing trends in customer impact and cost, helping management make informed decisions on resource allocation and understand the true cost of application failures. Together, these capabilities help organizations move from isolated technical signals to business-aware insights, decisions, and actions. To learn more, refer to Cross-domain intelligence with Azure Monitor data in Microsoft Fabric (Preview). Until next month That's a wrap for the July 2026 Microsoft Fabric Monthly Update. As always, we'll continue sharing new capabilities, enhancements, and improvements across Microsoft Fabric in future monthly updates. Thank you for being part of the Fabric community!
A new analytics frontier: GPU-accelerated Fabric Data Warehouse (Early Access Preview)
As data volumes grow, concurrency rises, and analytics workloads become more dynamic and AI-driven, performance becomes harder to predict and harder to scale. Every query sits in the critical path, adding pressure to the warehouse, and every second counts. This is the core tension in analytics today. The expectations have changed, but the underlying technology has not, leaving agents, applications, and AI systems waiting on data. To meet this moment, analytics needs a new kind of execution engine.
