{"data":[{"active":true,"blog_title":"Fast copy in Dataflows Gen2","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/fast-copy-in-dataflows-gen-2","feature_description":"Partitioned Compute is a capability of Dataflow Gen2 that enables parts of a dataflow to run in parallel, reducing the time to finish its evaluations.Partitioned compute targets scenarios where the Dataflow engine can efficiently fold operations that can partition the data source and process each partition in parallel. For example, in a scenario where you're connecting to multiple files stored in an Azure Data Lake Storage Gen2, you can partition the list of files from your source, efficiently retrieve the partitioned list of files using query folding, use the combine files experience, and process all files in parallel.By moving to GA, Partitioned Compute becomes a stable, supported foundation for performant and highly scalable data transformation in Fabric.","feature_name":"Dataflows - Dataflows Gen2 Partitioned Compute","last_modified":"2026-04-16","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"2026-06-02","release_item_id":"385260b0-5b07-ef11-9f89-000d3a34b75c","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"IDENTITY Columns in Fabric Data Warehouse (Preview)","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/identity-columns-in-fabric-data-warehouse-preview","feature_description":"Identity columns are numeric columns that automatically increment with each new row value that is inserted into a table with an Identity column defined.","feature_name":"Identity columns","last_modified":"2026-04-15","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"2026-07-31","release_item_id":"d7c986cd-1322-f011-998a-0022480939f0","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Announcing Data Clustering in Fabric Data Warehouse (Preview)","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/announcing-data-clustering-in-fabric-data-warehouse-preview","feature_description":"Data Clustering enables faster read performance by allowing users to specify columns for co-locating data on ingestion and perform file skipping on read.","feature_name":"Data Clustering","last_modified":"2026-04-15","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"2026-07-31","release_item_id":"5cd4b9a0-1322-f011-998a-0022480939f0","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Build real-time order notifications with Eventstream\u2019s CDC connector","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/build-real-time-order-notifications-with-eventstreams-cdc-connector","feature_description":"The Oracle CDC connector for Eventstream captures database changes from Oracle Database and streams them into Eventstream for real-time processing and analysis.","feature_name":"Eventstream Connector: Oracle DB CDC","last_modified":"2026-04-15","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"2026-06-30","release_item_id":"261ebeae-e420-f011-9989-000d3a302e4a","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"Allow users to create custom groups for their open tabs","feature_name":"Organize multitasking tabs in groups","last_modified":"2026-04-15","product_id":"c6da6b3b-ded6-ee11-9079-000d3a310f67","product_name":"Fabric Developer Experiences","release_date":"2026-05-18","release_item_id":"a5e7e388-2eba-f011-bbd3-000d3a30273e","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"Currently, users can open multiple workspaces simultaneously and switch between them via the navigation bar. However, the order of these workspace icons is fixed based on the sequence in which they were opened.This creates friction for users who frequently multitask across workspaces and want to organize their open items in a way that better reflects their workflow priorities.This feature allows users to reorder open workspace icons in the navigation bar through drag-and-drop interaction, enabling greater control and efficiency in multitasking.","feature_name":"Allow users to reorder open workspaces in the nav bar","last_modified":"2026-04-15","product_id":"c6da6b3b-ded6-ee11-9079-000d3a310f67","product_name":"Fabric Developer Experiences","release_date":"2026-04-20","release_item_id":"11114dc9-41ba-f011-bbd3-000d3a30273e","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Fabric Spark Monitoring APIs (Generally Available)","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/general-availability-announcement-fabric-spark-monitoring-apis","feature_description":"This feature provides an integrated, real-time dashboard for monitoring Spark application performance at both the driver and executor levels. Users can visualize CPU, memory, and core utilization across running and completed Spark applications--whether triggered through interactive notebooks or batch jobs. The dashboard aligns with the Fabric SaaS experience and enhances visibility into Spark vCore allocation and utilization.Users can inspect performance metrics at any moment in the application lifecycle, analyze utilization patterns, and access recommended actions to address bottlenecks. In addition to real-time insights, the experience includes summaries of active jobs and tasks, detailed Spark compute configurations, and the ability to drill into the Spark UI, application history, job-level details, or code-level snapshots.","feature_name":"Fabric Spark Real-Time Performance Monitoring for CPU, Memory, and vCores","last_modified":"2026-04-14","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"2026-07-31","release_item_id":"9f72b035-86ba-f011-bbd3-6045bd00f9db","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Govern in OneLake Catalog for Fabric admins (Preview)","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/onelake-catalog-govern-for-fabric-admins","feature_description":"The Govern tab in OneLake Catalog serves as a central hub for data owners to access insights, recommended actions, and available Fabric solutions. The solution now also supports Fabric admins with similar capabilities. We plan to expand the govern experience to include central bulk management features such as policies, domains, capacities, and tags. By combining insights, recommended actions, and flexbile management capabilities, Admins can boost their effectiveness and strengthen organizational governance.","feature_name":"OneLake Catalog - Govern with centralized management capabilities","last_modified":"2026-04-13","product_id":"796a0af7-2dc7-ee11-9079-000d3a3419a8","product_name":"Administration, Governance and Security","release_date":"2026-10-30","release_item_id":"184cc3ee-22b8-f011-bbd3-000d3a3740cc","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"The goal of this work is to make real-time dashboards smarter and more cost-efficient by: Refreshing only when new data is ingested in the underlying sources, using lightweight detection queries based on ingestion_time(). Reducing redundant queries and system load while ensuring users always see up-to-date insights when they open or monitor a dashboard. Providing a seamless fallback for data sources/tables that do not support ingestion_time().","feature_name":"RTD Live Update feature","last_modified":"2026-04-12","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"2026-04-30","release_item_id":"92bffcc0-bf04-f111-8406-6045bd0066ad","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Creating a Real Time Dashboard (RTD) using Copilot","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/creating-a-real-time-dashboard-rtd-using-copilot","feature_description":"","feature_name":"RTD time series visualization","last_modified":"2026-04-12","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"2026-04-30","release_item_id":"5a776051-c004-f111-8406-6045bd0066ad","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"New Dataflow Gen2 data destinations and experience improvements","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/new-dataflow-gen2-data-destinations-and-experience-improvements","feature_description":"We are introducing Google Cloud Storage (GCS) as a new data destination for Dataflow Gen2 in Preview, enabling customers to land transformed data from Microsoft Fabric directly into Google Cloud Storage using Dataflow Gen2's low-code Power Query experience.This preview expands Dataflow Gen2's destination ecosystem to better support multi-cloud data architectures, giving customers with existing investments in Google Cloud a simple way to integrate Fabric-based transformations into their broader data estate.Key benefits and scenarios:* Publish curated outputs from Dataflow Gen2 directly to Google Cloud Storage buckets* Support multi-cloud ingestion and data sharing scenarios while centralizing transformation logic in Fabric* Enable teams to prepare and standardize data in Fabric before making it available to GCP-based analytics, processing, or downstream pipelinesDuring Preview, the Google Cloud Storage data destination is intended for evaluation and feedback, allowing customers to validate connectivity patterns, performance characteristics, and integration workflows ahead of broader production use.This release is part of our broader effort to make Dataflow Gen2 a flexible, low-code transformation layer across clouds, with ongoing investments planned to further mature and expand multi-cloud destination support in Fabric.","feature_name":"Dataflows - New Destination: Google Cloud Storage","last_modified":"2026-04-11","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"2026-09-29","release_item_id":"45a4fc04-9ab3-f011-bbd3-000d3a30273e","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Simplifying Medallion Implementation with Materialized Lake Views in Fabric","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/announcing-materialized-lake-views-at-build-2025","feature_description":"Multi Lakehouse support in Lineage enables users to visualize and manage dependencies of Materialized Lake Views (MLVs) across multiple workspaces and lakehouses, providing a unified view that helps prevent data silos and improves transparency. It is designed to support scalable lineage tracking, advanced search, and focused navigation, making it easier for data teams to trace upstream and downstream dependencies.","feature_name":"Fabric Materialized Lake Views - Multi Workspace/Lakehouse support in Lineage","last_modified":"2026-04-09","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"2026-05-29","release_item_id":"f152e5fd-1fbf-f011-bbd3-000d3a3740cc","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Capacity Scheduler: Smarter capacity control for Eventhouse (Preview)","blog_url":"https://blog.fabric.microsoft.com/en-US/blog/capacity-scheduler-smarter-capacity-control-for-eventhouse-preview","feature_description":"The Eventhouse Min-Capacity Planner helps customers understand and forecast the impact of their Minimum Consumption (Min Capacity) settings on both performance and cost. It models how Eventhouse behaves when a minimum CU level is defined and allows users to schedule different Min-Capacity values per hour of the day and per day of the week. By visualizing baseline consumption, peak-time protection, and off-hours reduction, the planner makes it easy to choose the right minimum levels, avoiding throttling during busy periods while preventing unnecessary consumption during quiet times. It turns Min-Capacity scheduling into a simple, predictable, and actionable planning tool.","feature_name":"Capacity panner","last_modified":"2026-04-09","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"2026-05-12","release_item_id":"d1728a35-5ef1-f011-8406-6045bd026004","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"Quickly identify when and where throttling is occurring across your Eventhouse system. This feature highlights recent throttling events, helping you diagnose performance bottlenecks and take corrective action before they impact users.","feature_name":"show Throttling events of eventhouse at Eventhouse WS monitoring","last_modified":"2026-04-09","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"2026-05-05","release_item_id":"2d60a7e8-b555-f011-877a-00224804ca88","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Introducing Eventhouse Endpoint for Fabric Data Warehouse: Real-Time Analytics, Unified Architecture","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/introducing-eventhouse-endpoint-for-fabric-data-warehouse-real-time-analytics-unified-architecture","feature_description":"The Eventhouse endpoint is a powerful feature in Microsoft Fabric that enables users to query tables with exceptional speed and ease. It allows for real-time insights across your data estate, supporting structured, semi-structured, and unstructured data analysis. This endpoint transforms Fabric into a true real-time analytics platform, combining the simplicity of batch analytics with the responsiveness of event streaming. Additionally, it extends the architecture to structured data sources, allowing users to query Fabric Data Warehouse tables in real-time using KQL. Overall, Eventhouse is designed for managing both streaming and batch data, making it a game-changer for large-scale data operations","feature_name":"Eventhouse Endpoint for DWH","last_modified":"2026-04-09","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"2026-04-30","release_item_id":"e2e8767a-5df1-f011-8406-6045bd026004","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Running Apache Airflow jobs seamlessly in Microsoft Fabric","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/running-apache-airflow-jobs-seamlessly-in-microsoft-fabric","feature_description":"Apache Airflow jobs in Fabric provide the most effective and easy way to build DAG workflows using our built-in Python code editor. Now that we have enabled workpsace identity (WI) support for our Fabric providers, orchestrating Fabric jobs from Airflow is super easy.","feature_name":"Airflow - Workspace Identity Support","last_modified":"2026-04-09","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"2026-03-31","release_item_id":"d5aeaf0b-2701-f111-8406-000d3a376137","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"User-Defined Functions (UDFs) allow you to encapsulate reusable, parameterized DAX logic into named functions that  can be called across measures, calculated columns, and visual calculations throughout your semantic model. UDFs  support both value and expression parameters, enabling you to centralize complex business logic--like tax  calculations or currency conversions--so it's written once and maintained in a single place. Functions can be created  in DAX Query View or TMDL View and appear as first-class objects under a dedicated "Functions" node in Model  Explorer","feature_name":"DAX User Defined Functions","last_modified":"2026-04-08","product_id":"642a8375-05fc-ee11-a1ff-000d3a341a60","product_name":"Power BI","release_date":"2026-06-01","release_item_id":"e48e6300-9a33-f111-88b4-6045bd00fc61","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Simplifying Data Ingestion with Copy job \u2013 Introducing Change Data Capture (CDC) Support (Preview)","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/simplifying-data-ingestion-with-copy-job-introducing-change-data-capture-cdc-support","feature_description":"The Change Data Capture (CDC) capability in Copy Job is generally available.","feature_name":"Copy Job -  Change Data Capture (CDC) capability GA","last_modified":"2026-04-07","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"2026-09-30","release_item_id":"c4e7283e-6821-f011-998a-0022480939f0","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"Ability to configure retention between 1 to 30 days","feature_name":"Configurable Retention between 1-120 days","last_modified":"2026-04-04","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"2027-05-05","release_item_id":"c13c3e13-0d22-f011-998a-0022480939f0","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"Ability to configure the data warehouse retention between 1 to 120 days;","feature_name":"Configurable Retention between 1-120 days","last_modified":"2026-04-04","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"2026-09-01","release_item_id":"dfb17dba-0d22-f011-998a-0022480939f0","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Workspace-Level Private Link in Microsoft Fabric (Generally Available)","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/announcing-general-availability-of-workspace-level-private-link-in-microsoft-fabric","feature_description":"Private Links give users an additional ability to connect to Fabric privately, allowing connectivity only from specific Azure VNETs and disallowing connectivity from others or from the internet.","feature_name":"Ontology private links","last_modified":"2026-04-03","product_id":"cef5a30d-562f-f011-8c4d-6045bd096d8f","product_name":"IQ","release_date":"2026-04-24","release_item_id":"a045d9a4-b602-f111-8406-6045bd0a8ec1","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"BULK INSERT in Fabric Data Warehouse","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/bulk-insert-statement-in-fabric-datawarehouse","feature_description":"Fabric Data Warehouse will support the bcp utility and the TDS Bulk Load API, enabling high-performance data ingestion from a variety of client tools such as bcp, SSIS, and Azure Data Factory. This integration simplifies bulk data loading into Fabric DW and supports scalable, efficient workflows. Centralized support for these APIs ensures consistency across ingestion pipelines and improves interoperability with existing tools.<br/>An example of a bcp command that loads file content into a DW table:<br/>```bcp dbo.artists in gold_artist.txt -d TextDW -c -S myworkspace.datawarehouse.fabric.microsoft.com -G -U theuser@microsoft.com ```","feature_name":"BCP","last_modified":"2026-04-02","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"2026-09-21","release_item_id":"d58f4693-ca80-ef11-ac21-6045bd062aa2","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Create Metadata Driven Data Pipelines in Microsoft Fabric","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/create-metadata-driven-data-pipelines-in-microsoft-fabric","feature_description":"A commonly used functionality in ADF is configuring a Pipeline to run when a set of dependencies are met. We are excited to bring this functionality to Data Pipelines in Data Factory in Fabric to continue enriching orchestration capabilities.","feature_name":"Pipelines - Run Pipeline when dependencies are met","last_modified":"2026-04-01","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"2026-09-30","release_item_id":"f8636911-7191-ef11-ac21-002248098a98","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Announcing preview of Workspace Monitoring","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/announcing-public-preview-of-workspace-monitoring","feature_description":"We will enhance the Diagnostics experience for Apache Airflow jobs developers by surfacing logs in the Fabric Workspace Monitoring experience.","feature_name":"Airflow - Workspace logs integration for Diagnostics","last_modified":"2026-04-01","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"2026-09-29","release_item_id":"34a3246d-5921-f011-9989-000d3a329ecb","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Resolving Write Conflicts in Microsoft Fabric Data Warehouse","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/concurrency-control-and-conflict-resolution-in-microsoft-fabric-data-warehouse","feature_description":"This feature is part of the concurrency control strategy for Fabric DW. File-level write-write conflict detection is a mechanism designed to prevent two concurrent transactions from modifying the same physical file in a data warehouse at the same time. More precise than table-level detection (which blocks any concurrent changes to the same table).What It Does?Scope: Operates at the file level (e.g., Parquet files in Fabric DW), rather than at the table or row level.Goal: Detect overlapping changes (updates, deletes) to the same file during concurrent transactions.Trigger: When a transaction tries to commit changes, the system checks if any newer manifests indicate modifications to the same file since the transaction started.Lays groundwork for even finer-grained detection (row-level) in later releases.","feature_name":"File-Level write-write conflict detection (Generally Available)","last_modified":"2026-04-01","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"2026-09-08","release_item_id":"b7e155c6-94bf-f011-bbd3-000d3a5b0efa","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"Fabric workspace connection strings often contain complex, encoded server names that make it difficult for developers to identify or manage connections easily.With Friendly workspace server name, developers can configure a friendly server name for a workspace that helps connections simpler to manage.","feature_name":"Workspace-Friendly Connections","last_modified":"2026-04-01","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"2026-07-30","release_item_id":"be34d186-0cb9-f011-bbd3-6045bd05dd14","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"The Data Grid feature for data warehouses and SQL analytics endpoints includes:* Data Handling: Supports large datasets with virtualization for performance.* Column & Row Features: Resize, reorder, hide/show, group, pin, define dynamic columns, sort, filter, and select multiple rows.* Performance Optimization: Virtual scrolling, row virtualization, and lazy loading.* Filtering & Sorting: Multi-column sorting and custom filters for data types (string, integer, date).* Search: Search across columns.* Grid-Level Options: Copy data with/without headers, select multiple rows, and export data in various formats.","feature_name":"Data Grid Improvements in Data Warehouse & SQL Endpoint","last_modified":"2026-04-01","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"2026-05-29","release_item_id":"9d7d9b3d-0d77-f011-b4cb-000d3a329ecb","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Evaluate Power Query Programmatically in Microsoft Fabric (Preview)","blog_url":"https://blog.fabric.microsoft.com/en-US/blog/execute-power-query-programmatically-in-microsoft-fabric","feature_description":"Modern Get Data offers highly intuitive methods for users to connect to their data from a wide range of data sources. By integrating Modern Get Data with Power BI Desktop, users will also be able to easily discover and consume data stored in Fabric via the OneLake Catalog which is now deeply integrated within the Modern Get Data experience. This Modern Get Data experience facilitates an efficient process for importing data into Power BI reports to enable self-service data analytics.","feature_name":"Power Query - Modern Power Query Get Data in Power BI Desktop","last_modified":"2026-04-01","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"2026-05-18","release_item_id":"ecdec705-121b-f011-9989-6045bd03a542","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Welcome to Fabric Data Warehouse","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/welcome-to-fabric-data-warehouse","feature_description":"We are updating the TDS (Tabular Data Stream) connection behavior in Fabric Warehouse to ensure consistent and predictable connections based on the specified Initial Catalog value.Previously, connections with an empty or invalid database name could redirect users to a random data warehouse. With this update, connections now honor the target database explicitly or throw validation errors when the database does not exist.","feature_name":"Connection Behavior When Connecting to Fabric Warehouse","last_modified":"2026-04-01","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"2026-04-30","release_item_id":"512eea0a-39bf-f011-bbd3-00224808fcf0","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Tenant level private link support for Microsoft Fabric API for GraphQL (Generally Available)","blog_url":"https://blog.fabric.microsoft.com/en-US/blog/34710","feature_description":"Fabric Graph supports private connectivity using Fabric private endpoints and private links. This allows customers to access graph workloads over private networks, keep traffic off the public internet, and deploy Fabric Graph in locked down enterprise network environments. The service validates graph APIs under private access configurations and propagates the required access context for trusted service to service communication.","feature_name":"Fabric Graph supports private network access for enterprise environments","last_modified":"2026-03-31","product_id":"cef5a30d-562f-f011-8c4d-6045bd096d8f","product_name":"IQ","release_date":"2026-05-04","release_item_id":"fc241721-2402-f111-8406-000d3a36696c","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Introducing Graph in Microsoft Fabric \u2013 Connected Data for the Era of AI","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/graph-in-microsoft-fabric","feature_description":"Fabric Graph is designed to operate at very large scale, supporting graphs with billions of elements while maintaining predictable performance and reliability. Ongoing investments improve query stability, ingestion throughput, and service resiliency. Operational visibility is enhanced by exposing detailed ingestion failure information and clearer diagnostic signals, enabling customers to identify, understand, and resolve issues more quickly as graph workloads scale into production.","feature_name":"Fabric Graph delivers reliable performance at billion scale","last_modified":"2026-03-31","product_id":"cef5a30d-562f-f011-8c4d-6045bd096d8f","product_name":"IQ","release_date":"2026-05-04","release_item_id":"e7719142-2202-f111-8406-000d3a36696c","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Introducing Graph in Microsoft Fabric \u2013 Connected Data for the Era of AI","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/graph-in-microsoft-fabric","feature_description":"Fabric Graph is now Generally Available, delivering a secure, scalable, and enterprise-ready graph analytics capability powering Fabric IQ and natively integrated with Microsoft Fabric.Fabric Graph is engineered to operate at billion-scale, providing predictable performance, improved ingestion throughput, resilient query execution, and enhanced diagnostic visibility to support production-grade graph workloads. It expands GQL feature coverage to support advanced relationship modeling scenarios, including nested data, OPTIONAL MATCH, extended FOR semantics, result shaping functions, quantified edge patterns, and shortest-path queries.Fabric Graph lowers the barrier to graph adoption with no-code and low-code experiences for modeling, querying, and exploration, while still enabling full GQL control for advanced users. It also integrates in public preveiw with Fabric Data Agent and supports natural language to GQL (NL2GQL), allowing users to query connected data through conversational experiences while maintaining transparency through executable query inspection.From a governance and security standpoint, Fabric Graph will shortly enforce OneLake security policies end-to-end, including row-, column-, and object-level controls, ensuring consistent data governance without introducing alternate access paths. 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