{"data":[{"active":true,"blog_title":null,"blog_url":null,"feature_description":"Fabric feature store is a centralized system that manages the data inputs, called features, that power machine learning models. It bridges raw data and ML models, giving teams one place to define, store, serve, and discover features instead of each team building its own pipelines.","feature_name":"Feature Store","last_modified":"2026-08-11","product_id":"0522b590-dcd6-ee11-9079-000d3a310f67","product_name":"Data Science","release_date":"Q4 2026","release_item_id":"75004e12-2158-f111-bec7-6045bd00f798","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":"2026-08-05","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"Q4 2026","release_item_id":"2d60a7e8-b555-f011-877a-00224804ca88","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Introducing the HTTP and MongoDB CDC Connectors for Eventstream \u2014 Inspired by You","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/introducing-the-http-and-mongodb-cdc-connectors-for-eventstream-inspired-by-you","feature_description":"This feature enables customers to develop or reuse their own Kafka connectors, or open-source Kafka connectors, when no pre-built streaming connector is available.With this capability, you can:- Customize your own connector at your own pace.- Run with no infrastructure to manage.- Reuse open-source connectors directly.","feature_name":"Eventstream supports customers to upload own connector","last_modified":"2026-08-05","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"Q3 2026","release_item_id":"60246c0e-762f-f011-8c4d-000d3a34671f","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"Identify how applications like Activator and QuerySet contribute to Eventhouse activity. Analyze query volume and activity by application and identify unusual spikes to support informed capacity management.","feature_name":"Monitor Eventhouse activity by application","last_modified":"2026-08-05","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"Q2 2026","release_item_id":"38e581bb-4541-f111-88b5-6045bd0a8ec1","release_status":"Shipped","release_type":"General availability"},{"active":true,"blog_title":"Eventhouse Accelerated OneLake Table Shortcuts (Generally Available)","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/eventhouse-accelerated-onelake-table-shortcuts-generally-available","feature_description":"You can now join data from OneLake shortcuts in update policies. 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 duration must cover all data.","feature_name":"Eventhouse Update Policies referencing Accelerated Shortcuts","last_modified":"2026-08-04","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"Q3 2026","release_item_id":"a144449e-0780-f111-ab0f-6045bd0a8ec1","release_status":"Shipped","release_type":"General availability"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"Branch workspace admin profile (aka. admin delegation) lets developers use the branch-out experience without needing create-workspace or assign-capacity permissions. Workspace admins keep full control through pre-configured guardrails while unblocking developer workflows. Capabilities:  - Developers without create-workspace or assign-capacity permissions can perform branch-out seamlessly  - Admins pre-configure profile to ensure guardrails for branch workspaces: developer role, capacity assignment and admin list.","feature_name":"Git Integration - Branch workspace admin profile","last_modified":"2026-08-03","product_id":"c6da6b3b-ded6-ee11-9079-000d3a310f67","product_name":"Fabric Developer Experiences","release_date":"Q3 2026","release_item_id":"79538350-662c-f111-88b4-6045bd0a886d","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"List slicer with drop down mode and advanced search options.","feature_name":"List slicer with dropdown mode","last_modified":"2026-07-31","product_id":"642a8375-05fc-ee11-a1ff-000d3a341a60","product_name":"Power BI","release_date":"Q4 2026","release_item_id":"ba851f2d-d21f-f111-8341-6045bd0a8ec1","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"Conditional formatting to be available for series in Power BI visuals. This includes lines, markers, and legends.","feature_name":"Conditional formatting for lines and series/labels in Power BI visuals","last_modified":"2026-07-31","product_id":"642a8375-05fc-ee11-a1ff-000d3a341a60","product_name":"Power BI","release_date":"Q3 2026","release_item_id":"853c11fa-d11f-f111-8341-6045bd0a8ec1","release_status":"Shipped","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":"2026-07-30","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"Q4 2026","release_item_id":"5cd4b9a0-1322-f011-998a-0022480939f0","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Introducing support for Workspace Identity Authentication in Fabric Connectors","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/announcing-support-for-workspace-identity-authentication-in-new-fabric-connectors-and-for-dataflow-gen2","feature_description":"Workspace identity auth support for Eventstream Event hub source. Customers can use workspace identity to do the authentication between Eventstream and their event hub source.","feature_name":"Workspace identity auth support for Eventstream Event hub source","last_modified":"2026-07-30","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"Q3 2026","release_item_id":"246e8c98-903f-f111-88b5-6045bd00f798","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Mastering Declarative Data Transformations with Materialized Lake Views","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/mastering-declarative-data-transformations-with-materialized-lake-views","feature_description":"Fabric data engineers today write 80-100+ lines of boilerplate PySpark to ingest CSV and Parquet files into Delta tables - handling file discovery, schema inference, incremental refresh, schema drift, and error recovery manually. This feature introduces a simple Spark SQL DDL surface (CREATE MATERIALIZED LAKE VIEW ... FROM OneLake_Files OPTIONS (...)) that declaratively handles all of this. It includes automatic schema evolution (or strict fixed-schema mode), three refresh modes (append_only, mirror, full), built-in error handling, structured user telemetry on files and lakehouse-level DAG lineage.","feature_name":"Declarative file data ingestion experience in Fabric Materialized Lake Views","last_modified":"2026-07-30","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q3 2026","release_item_id":"14254506-0f3f-f111-88b5-6045bd00f798","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Time-Travelling through data: The Magic of Table clones","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/time-travelling-through-data-the-magic-of-table-clones","feature_description":"Ability to clone warehouse across workspaces","feature_name":"Warehouse Clones","last_modified":"2026-07-29","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"Q1 2027","release_item_id":"e240d768-fc21-f011-998a-0022480939f0","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Introducing the New Deployment Pipelines design: A leap forward in deployment Efficiency","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/introducing-the-new-deployment-pipelines-design-a-leap-forward-in-deployment-efficiency","feature_description":"**Release:** Public Preview  |  **Product area:** Fabric CI/CD**Deployment plan** is a new first-class Fabric workspace item that lets you **define, order, and automate** how your content is deployed across environments.Instead of relying on system-inferred lineage order, you author a plan on a **visual canvas** (with **YAML as the source of truth**) to:- **Declare the exact sequence** in which items deploy- **Add pre and post deployment steps** -- for example, running a Notebook to hydrate a Lakehouse, or a Data Pipeline before dependent items go liveDeployment plans **attach to the deployment operations available in Fabric**, including **Git** (branch-out, initial sync, update from Git), **Deployment Pipelines**, **Bulk Import**, and the **CI/CD library**, and are **honored at deploy time**.The result: every promotion follows your intended orchestration and produces a **working, reproducible solution**, not just a set of deployed definitions.","feature_name":"Deployment Plan","last_modified":"2026-07-29","product_id":"c6da6b3b-ded6-ee11-9079-000d3a310f67","product_name":"Fabric Developer Experiences","release_date":"Q4 2026","release_item_id":"fbee65ff-2b8b-f111-ab0f-6045bd03ee11","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Configurable Data Retention in Microsoft Fabric Warehouse (Preview)","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Configurable-Data-Retention-in-Microsoft-Fabric-Warehouse/ba-p/5181211","feature_description":"Ability to configure the data warehouse retention between 1 to 120 days;","feature_name":"Configurable Retention between 1-120 days","last_modified":"2026-07-29","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"Q4 2026","release_item_id":"dfb17dba-0d22-f011-998a-0022480939f0","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Data Warehouse Utilization Reporting in Fabric Capacity Metrics App","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/data-warehouse-utilization-reporting-in-fabric-capacity-metrics-app","feature_description":"Shortcuts in Fabric DW allow users to directly access and query data that is present in internal and external data sources, without loading them into Fabric DW. Users will have the capability to create Table Shortcuts via TSQL & UX","feature_name":"Shortcuts in Fabric Data Warehouse (Public Preview)","last_modified":"2026-07-29","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"Q4 2026","release_item_id":"ce7f2f21-9421-f011-9989-000d3a302e4a","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":"2026-07-29","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"Q4 2026","release_item_id":"b7e155c6-94bf-f011-bbd3-000d3a5b0efa","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Large string and binary values in Fabric Data Warehouse and SQL analytics endpoint for mirrored items (Generally Available)","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/large-string-and-binary-values-in-fabric-data-warehouse-and-sql-analytics-endpoint-for-mirrored-items-general-availability","feature_description":"SQL analytics endpoints of Lakehouses will support large string and binary data using VARCHAR(MAX) and VARBINARY(MAX) types up to 16 MB.","feature_name":"VARCHAR(MAX) support in SQL analytics endpoint of Lakehouses","last_modified":"2026-07-29","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"Q3 2026","release_item_id":"f4c0ce02-2e61-f011-bec2-000d3a302e4a","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"New Features and Enhancements for Virtual Network Data Gateway","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/new-features-and-enhancements-for-virtual-network-data-gateway","feature_description":"For VNet data gateways, the fixed node setup can be inefficient during high-demand periods. To improve this, we're introducing auto-scaling, which dynamically adjusts the number of nodes based on demand.","feature_name":"Gateways - Autoscaling gateway clusters for VNet Gateway","last_modified":"2026-07-29","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"78dc21fc-7420-f011-998a-0022480939f0","release_status":"Shipped","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 PostgreSQL as a new data destination for Dataflow Gen2, enabling customers to publish transformed data from Microsoft Fabric directly into PostgreSQL databases using Dataflow Gen2's low-code Power Query experience.This release expands Dataflow Gen2's destination ecosystem to better support operational, analytical, and application integration scenarios, giving customers a simple and flexible way to operationalize Fabric-based data transformations in PostgreSQL environments.Key benefits and scenarios:* Publish curated outputs from Dataflow Gen2 directly into PostgreSQL databases and tables* Support operational reporting, application integration, and downstream analytics scenarios using PostgreSQL as a serving layer* Enable teams to prepare, standardize, and reshape data in Fabric before delivering it to PostgreSQL-based applications, services, or analytics workloadsThe PostgreSQL data destination helps customers simplify data movement and reduce the need for custom pipelines by combining Fabric's scalable transformation capabilities with PostgreSQL's broad ecosystem compatibility and adoption.This release is part of our broader effort to make Dataflow Gen2 a flexible, low-code transformation layer across data platforms, with continued investments planned to expand destination support and simplify end-to-end data integration experiences in Fabric.","feature_name":"Dataflows - New Destination: PostgreSQL","last_modified":"2026-07-29","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"78a9449f-c85e-f111-a826-000d3a366c85","release_status":"Shipped","release_type":"Public preview"},{"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 link allows customers to connect to their SQL DBs via a private endpoint from the customer's virtual network. Workspace level private links will give customers the flexibility to use private link connection to specific workspace(s) which have high security requirements and contain sensitive data while their other workspace can be open to public.","feature_name":"Workspace level Private Link for SQL database","last_modified":"2026-07-29","product_id":"347da228-ea54-ef11-a317-0022480a694f","product_name":"SQL database","release_date":"Q3 2026","release_item_id":"744ac485-321d-f011-9989-000d3a302e4a","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Introducing Planning in Microsoft Fabric IQ: From historical data to forecasting the future","blog_url":"https://blog.fabric.microsoft.com/en-US/blog/introducing-planning-in-microsoft-fabric-iq-from-historical-data-to-forecasting-the-future","feature_description":"Planning in Microsoft Fabric is a native enterprise planning capability that brings budgeting, forecasting, and scenario modeling directly into your data platform--eliminating silos between planning, analytics, and AI.Built into Microsoft Fabric, Planning enables business and data teams to collaboratively create, manage, and refine plans on top of governed, real-time data--powering faster, more informed decision-making across the organization.","feature_name":"Fabric Planning","last_modified":"2026-07-29","product_id":"cef5a30d-562f-f011-8c4d-6045bd096d8f","product_name":"IQ","release_date":"Q3 2026","release_item_id":"69db5ef2-7f01-f111-8406-000d3a36696c","release_status":"Shipped","release_type":"General availability"},{"active":true,"blog_title":"Outbound access protection for Data Factory (Generally Available)","blog_url":"https://blog.fabric.microsoft.com/en-US/blog/outbound-access-protection-for-data-factory-generally-available","feature_description":"We are delivering Outbound Access Protection for Operations Agent in Microsoft Fabric, enabling administrators to enforce workspace-level security controls on all outbound actions performed by AI-driven automation.With this capability, you canEnsure all outbound actions such as triggering Power Automate flows, executing Fabric jobs, or sending Teams notifications are governed by administrator-defined connection rules.Block outbound connections by default when OAP is enabled, permitting traffic only through approved endpoints to prevent unauthorized data movement.Leverage a hybrid enforcement model that applies direct checks for external services like Teams while using Activator-managed enforcement for workflow and job execution across workspaces.Outbound Access Protection is available in Public Preview for all Fabric customers using Operations Agent. Configure access rules from the workspace security settings.Tentative GA Scope: Activator OAP toggle for OAOntology OAP toggle for OAPower Automate OAP toggle for OA","feature_name":"Outbound access protection for Operations Agent","last_modified":"2026-07-29","product_id":"cef5a30d-562f-f011-8c4d-6045bd096d8f","product_name":"IQ","release_date":"Q3 2026","release_item_id":"675ce012-f144-f111-88b5-6045bd006301","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Using Bulk Copy API for faster ingestion in Fabric Data Warehouse (Preview)","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Using-Bulk-Copy-API-for-faster-ingestion-in-Fabric-Data/ba-p/5195627","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":"2026-07-29","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"Q2 2026","release_item_id":"d58f4693-ca80-ef11-ac21-6045bd062aa2","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"Fabric Data Warehouse will introduce string similarity and comparison functions based on Levenshtein and Jaro-Winkler algorithms. These functions make it easier to find strings that are similar, even when they have small changes or spelling errors.New functions that will be added are:* EDIT_DISTANCE - Returns the number of edits (insertions, deletions, substitutions) needed to transform one string into another.* EDIT_DISTANCE_SIMILARITY - Calculates a similarity score (0-1) based on Levenshtein distance, where 1 means identical strings.* JARO_WINKLER_DISTANCE - Measures the distance between two strings using the Jaro-Winkler algorithm, considering transpositions and common prefixes.* JARO_WINKLER_SIMILARITY - Returns a similarity score (0-1) using Jaro-Winkler, optimized for short strings and minor typos.","feature_name":"Fuzzy string matching","last_modified":"2026-07-29","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"Q2 2026","release_item_id":"9b0e4fae-ecb8-f011-bbd3-000d3a30273e","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"New metadata sync and more in SQL Analytics Endpoint (Preview)","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/New-metadata-sync-and-more-in-SQL-Analytics-Endpoint-Preview/ba-p/5183137","feature_description":"New version of the metadata sync for SQL analytics endpoint built on a new architecture. This delivers,- Data refresh in seconds, not minutes- Ability to turn on this feature for a workspace- Support for v2 checkpoints (at GA)- Multiple performance improvements","feature_name":"SQL Analytics Endpoint - New Metadata sync","last_modified":"2026-07-29","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"Q2 2026","release_item_id":"511823c2-744d-f111-bec7-002248085b3f","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Announcing SQL database in Microsoft Fabric (Preview)","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/announcing-sql-database-in-microsoft-fabric-public-preview","feature_description":"Users should be able to restore a database from backups of a deleted database in their workspaces.","feature_name":"Fabric SQL | Point-in-time restore from dropped databases REST APIs ONLY | Public Preview","last_modified":"2026-07-29","product_id":"347da228-ea54-ef11-a317-0022480a694f","product_name":"SQL database","release_date":"Q2 2026","release_item_id":"49834652-a41b-f011-9989-000d3a34671f","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Extending Point-in-Time Retention in Fabric SQL DB: From 7 to 35 Days","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/extending-point-in-time-retention-in-fabric-sql-db-from-7-to-35-days","feature_description":"Backup retention policy is the capability to change the retention period which allows users to adjust the duration for which automated backups are maintained for a SQL database artifact. This feature directly affects point-in-time restore capability (specific period in which user can choose the specific point in time to restore their database to).","feature_name":"Fabric SQL | Public Preview | Extend Backup Retention Policy to 35 days","last_modified":"2026-07-29","product_id":"347da228-ea54-ef11-a317-0022480a694f","product_name":"SQL database","release_date":"Q3 2025","release_item_id":"608d20dd-a31b-f011-9989-000d3a34671f","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"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. We are migrating the Data Agent UX and Programmatic experiences to the Responses API","feature_name":"Data Agent Migration to Responses API","last_modified":"2026-07-27","product_id":"951b64e0-a663-f111-a826-6045bd00f798","product_name":"Conversational Analytics","release_date":"Q3 2026","release_item_id":"e9e7c616-d289-f111-ab0f-6045bd03e6d8","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"This feature delivers a comprehensive Notebook Run Lineage experience for Spark applications, enabling data engineers to monitor and analyze complex notebook workflows with ease. It provides an at-a-glance view of all notebook run statuses, helps quickly identify root-cause failures across nested executions, and highlights performance bottlenecks. Users can filter and explore large execution graphs, access detailed error and run-level information, and view aggregated summaries of execution health and resource usage--all in one place.","feature_name":"Track and Manage Notebook Run Dependencies & Linage View","last_modified":"2026-07-27","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q3 2026","release_item_id":"71469140-3550-f111-bec7-6045bd00fc61","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Fabric Runtime Release Channels","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Fabric-Runtime-Release-Channels/ba-p/5240330","feature_description":"This feature enables multiple runtime channels for customers. The default channel will remain the current standard runtime, while an EarlyAccess channel will provide the latest updates - such as library upgrades and security vulnerability fix..Using Spark configuration, customers can test and validate these changes early, before they become part of the default runtime channel.","feature_name":"Synapse Release Channel - Public Preview","last_modified":"2026-07-27","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q3 2026","release_item_id":"3665d185-5b43-f111-88b5-6045bd0a886d","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Announcing Eventhouse Query Acceleration for OneLake Shortcuts (Preview)","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/announcing-query-acceleration-for-onelake-shortcuts-public-preview","feature_description":"On the data exploration front, the new Lakehouse Query Explorer introduces a streamlined, in-context query experience directly within the Lakehouse. Users can write and run Spark SQL queries right from the Lakehouse explorer to quickly discover and analyze data, with intelligent suggestions and real-time error prevention all without needing to navigate to a notebook. Results can be saved as Spark Views, visualized as charts, filtered, sorted, and downloaded, with rich contextual information throughout. When deeper analysis is required, code can be seamlessly promoted into a notebook for more complex processing, bridging the gap between ad-hoc exploration and production-grade data engineering.","feature_name":"Lakehouse Query Window","last_modified":"2026-07-27","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q2 2026","release_item_id":"e5bc7caf-294e-f111-bec7-000d3a376137","release_status":"Shipped","release_type":"General availability"},{"active":true,"blog_title":"Fabric Runtime 1.3 is Generally Available! Upgrade your data engineering and science workloads to harness the latest innovations and performance enhancements","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/fabric-runtime-1-3-is-generally-available-upgrade-your-data-engineering-and-science-workloads-to-harness-the-latest-innovations-and-performance-enhancements","feature_description":"We are upgrading the underlying operating system of Fabric Runtime 1.3 from Mariner 2.0 to Mariner 3.0, delivering improved security, performance, and long-term support for Apache Spark workloads. This upgrade brings the latest OS-level patches, enhanced container compatibility, and a modernized base image that aligns with the latest Azure infrastructure standards.To ensure a smooth transition, the upgrade leverages the Runtime Release Channel feature, which allows customers to preview and validate the Mariner 3.0-based runtime before it becomes the default. Data engineers can switch to the release candidate channel in their environment settings, run their existing workloads, and confirm compatibility -- all before the update is applied across their organization.Key benefits include:Enhanced security posture with the latest OS-level patches and hardeningImproved performance from the modernized Mariner 3.0 baseCustomer-controlled validation through the release channel, reducing risk of unexpected issuesContinued compatibility with all existing Runtime 1.3 workloads and librariesThe Mariner 3.0 upgrade is available through Fabric environment settings with no additional configuration required.Business Value: Strengthens the security and performance foundation of Fabric Runtime 1.3 by upgrading to Mariner 3.0, while giving customers the ability to validate the change before it rolls out to production.","feature_name":"Fabric Runtime 1.3 Mariner 3.0 Upgrade","last_modified":"2026-07-27","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q2 2026","release_item_id":"b8bed2a5-5943-f111-88b5-6045bd0a886d","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Fabric Runtime Release Channels","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Fabric-Runtime-Release-Channels/ba-p/5240330","feature_description":"This feature enables multiple runtime channels for customers. The default channel will remain the current standard runtime, while an EarlyAccess channel will provide the latest updates - such as library upgrades and security vulnerability fix..Using Spark settings in the environment, customers can test and validate these changes early, before they become part of the default runtime channel.","feature_name":"Fabric Release Channel - Public Preview","last_modified":"2026-07-27","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q2 2026","release_item_id":"399f07f8-96ba-f011-bbd3-00224808fcf0","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Configure and manage Activator rules directly in Eventstream (Preview)","blog_url":"https://blog.fabric.microsoft.com/en-US/blog/configure-and-manage-activator-rules-directly-in-eventstream-preview","feature_description":"The new Activator Alert feature in Eventstream allows users to set up alerts in a single step, streamlining the process. With this enhancement, customers can quickly create alerts directly within Eventstream, saving time and reducing complexity.","feature_name":"Eventstream Integrated Activator Alert Setup Experience","last_modified":"2026-07-24","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"Q3 2026","release_item_id":"3aac4279-ca3d-f111-88b5-6045bd0066ad","release_status":"Shipped","release_type":"General availability"},{"active":true,"blog_title":"Announcing the General Availability of Enhanced Eventstream and Connector Sources","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/announcing-the-general-availability-of-enhanced-eventstream-and-connector-sources","feature_description":"Eventstream now provides a Salesforce change data capture source connector, streaming record changes from Salesforce into Microsoft Fabric. Teams can react to business changes as they happen.With this capability, you can:- Stream Salesforce record changes into Eventstream in real time.- Build dashboards and alerts on customer and sales activity.- Route Salesforce changes to Fabric destinations without custom code.","feature_name":"Salesforce change data capture source connector","last_modified":"2026-07-23","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"Q1 2027","release_item_id":"f661c4f7-8d3f-f111-88b5-6045bd00f798","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"New Eventstream sources: MQTT, Solace PubSub+, Azure Data Explorer, Weather & Azure Event Grid","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/new-eventstream-sources-mqtt-solace-pubsub-azure-data-explorer-weather-event-grid","feature_description":"The Azure Data Explorer source connector for Eventstream extends ingestion limits and adds streaming from a custom query, so you can bring Azure Data Explorer data into Microsoft Fabric for further real-time processing.With this capability, you can:- Stream data from Azure Data Explorer into Eventstream.- Use a custom query to select the data you ingest.- Route results to Fabric destinations for downstream analytics.","feature_name":"Azure Data Explorer source connector for Eventstream","last_modified":"2026-07-23","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"Q1 2027","release_item_id":"34fee3ee-8e3f-f111-88b5-6045bd00f798","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Mirroring for Google BigQuery in Microsoft Fabric (Preview)","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/announcing-public-preview-mirroring-for-google-bigquery-in-microsoft-fabric","feature_description":"Mirroring for Google BigQuery enables customers to continuously replicate BigQuery data into Fabric OneLake, making it easier to use BigQuery data with Power BI, data engineering, data science, and AI experiences in Fabric. Customers can bring data from Google BigQuery into the Fabric analytics estate without maintaining separate ETL pipelines for common replication scenarios.","feature_name":"Mirroring - Google BigQuery","last_modified":"2026-07-23","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"e64919b9-5163-f111-a826-000d3a36696c","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"Build Agent with AI mode is expanding support for Semantic Models in Data Agent, helping users generate semantic-model specific instructions and example queries more easily. The feature can explore Semantic Model tables, measures, columns, and recent query patterns to produce more relevant configurations while preserving transparency through visible run steps and explored queries. Users remain in control by reviewing, refining, and committing generated configurations before they update their Data Agent setup.","feature_name":"Build Agent with AI Mode: Support Semantic Models","last_modified":"2026-07-22","product_id":"951b64e0-a663-f111-a826-6045bd00f798","product_name":"Conversational Analytics","release_date":"Q3 2026","release_item_id":"df9398eb-fb68-f111-a826-6045bd0066ad","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"Data agents now have advanced DAX generation that more accurately generates queries from your natural-language questions. This update introduces instance value indexing and delivers higher response accuracy and consistency.","feature_name":"Advanced DAX Generation for Semantic Models in Data Agents","last_modified":"2026-07-22","product_id":"951b64e0-a663-f111-a826-6045bd00f798","product_name":"Conversational Analytics","release_date":"Q3 2026","release_item_id":"681eef41-b566-f111-a826-6045bd0066ad","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"Build Agent with AI Mode file support lets users upload existing text-based business context such as documentation, glossaries, process guides, and data dictionaries, so Creator Agent can use it to generate more complete and accurate Data Agent configurations. This reduces repetitive setup work by allowing users to reuse knowledge that already exists in files, rather than manually recreating that context through long chat interactions.","feature_name":"Build Agent with AI Mode Supports Files as Context","last_modified":"2026-07-22","product_id":"951b64e0-a663-f111-a826-6045bd00f798","product_name":"Conversational Analytics","release_date":"Q3 2026","release_item_id":"54678565-fb68-f111-a826-6045bd0066ad","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Creator Improvements in the Data Agent","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/creator-improvements-in-the-data-agent","feature_description":"The Build Agent with AI mode is a specialized AI assistant designed to help data agent creators configure, improve, and optimize their data agents by generating and refining Agent Instructions, Data Source Instructions, and Few-Shot Examples. It addresses common customer pain points such as confusion about where to put instructions, uncertainty about the effectiveness of few shots, and difficulty diagnosing why an agent produces poor results. The agent works in a collaborative, chat-based 'setup' mode, where it analyzes existing configurations, explores database schemas and query patterns, and recommends improvements that users can explicitly accept or reject. It is designed to detect ambiguity and contradictions across configurations and suggest clearer, more consistent alternatives. Initially focused on SQL data sources, the Build Agent with AI mode is intended to expand to additional data sources (e.g., KQL, semantic models) over time. Overall, it enables a faster, more scalable, and more understandable way to build high-quality data agents without requiring deep knowledge of the underlying system.","feature_name":"Build Agent with AI Mode in Data Agent","last_modified":"2026-07-22","product_id":"951b64e0-a663-f111-a826-6045bd00f798","product_name":"Conversational Analytics","release_date":"Q2 2026","release_item_id":"db90d1e4-cbf0-f011-8407-002248096d54","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Inline Scalar user-defined functions (UDFs) in Microsoft Fabric Warehouse (Preview)","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/inline-scalar-user-defined-functions-udfs-in-microsoft-fabric-warehouse-preview","feature_description":"We're expanding scalar user-defined functions with new capabilities, including INLINE = AUTO, improved error messaging, support for special built-in functions, and CONTINUE/BREAK statements in **computation**-based UDFs.","feature_name":"Scalar User-defined functions (UDFs)","last_modified":"2026-07-21","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"Q4 2026","release_item_id":"edb33b5b-7f5d-f011-bec2-0022480939f0","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Inline Scalar user-defined functions (UDFs) in Microsoft Fabric Warehouse (Preview)","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/inline-scalar-user-defined-functions-udfs-in-microsoft-fabric-warehouse-preview","feature_description":"SQL native, computation-based scalar UDFs now support WHILE loops, multiple RETURN statements and deeply nested IF-THEN-ELSE blocks in the function body. They can also be used in queries that include Common Table Expressions (CTEs), and within GROUP BY, HAVING, and ORDER BY clauses.","feature_name":"Scalar User-defined functions (UDFs) - Procedural SQL","last_modified":"2026-07-21","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"Q3 2026","release_item_id":"c418b243-333f-f111-88b5-6045bd0a8ec1","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Announcing the Availability of REST APIs for Connections and Gateways in Microsoft Fabric","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/announcing-the-availability-of-rest-apis-for-connections-and-gateways-in-microsoft-fabric","feature_description":"New tenant admin REST APIs for end-to-end visibility and lifecycle management of all cloud connections in Microsoft Fabric -- list, get, delete, manage permissions/ take over.","feature_name":"Connections - Tenant Admin APIs for connections","last_modified":"2026-07-21","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"79c8ba5e-fd3c-f111-88b5-6045bd00f798","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Eventhouse Accelerated OneLake Table Shortcuts (Generally Available)","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/eventhouse-accelerated-onelake-table-shortcuts-generally-available","feature_description":"We are expanding update policies in Eventhouse to support referencing shortcuts aka. external tables backed by the Query Acceleration Policy (QAP) in Microsoft Fabric Real-Time Intelligence. With this update, you can build update policies that references or joins data directly from OneLake delta table and other supported storage locations.With this capability, you can:- Build richer transformations that join streaming data with reference data stored in OneLake, lakehouses, or external storage.","feature_name":"Eventhouse: Reference Accelerated Shortcuts in Update Policy","last_modified":"2026-07-20","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"Q3 2026","release_item_id":"4c1e5d6a-ff3f-f111-88b5-002248085b3f","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Supercharge your real-time data ingestion: What's new in Fabric Eventstream connectors","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Supercharge-your-real-time-data-ingestion-What-s-new-in-Fabric/ba-p/5260314","feature_description":"**Add Data Source Connector for Business Events in Real-Time Hub****Overview**Enable seamless ingestion of Business Events into Real-Time hub Eventstream--making it easier to onboard event producers and unify event streams across systems.**Key Capabilities*** Native Data Source Connectivity: Connect external systems and Fabric workloads as event producers through a unified Real-Time hub experience.* Simplified Event Ingestion: Stream Business Events directly from connected data sources without requiring custom integration layers.* Schema-Aware Mapping: Map incoming data streams to standardized Business Event schemas to ensure consistency across producers and consumers.* Centralized Event Discovery: Surface all ingested Business Events in Real-Time hub for easy exploration, filtering, and subscription.* Seamless Integration with Downstream Consumers: Enable immediate consumption by Eventstream.","feature_name":"Add Data Source Connector for Business Events in Real-Time Hub","last_modified":"2026-07-17","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"Q3 2026","release_item_id":"ddd81a72-da3d-f111-88b5-6045bd0a8ec1","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Acquiring Real-Time Data from New Sources with Enhanced Eventstream","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/acquiring-real-time-data-from-new-sources-with-enhanced-eventstream","feature_description":"**Eventstream as a Business Events Publisher from Real-Time Hub****Overview**Enable Eventstream to act as a native publisher of Business Events directly from Real-Time hub--simplifying event creation and allowing users to emit business signals from streaming data with minimal setup.**Key Capabilities*** Native Eventstream Publishing from Real-Time Hub: Create and configure Eventstream publishers directly from the Business Events experience in Real-Time hub.* Seamless Stream-to-Event Mapping: Map incoming data streams to Business Event schemas using built-in mappers to transform raw signals into meaningful business events.* Low-Code Publisher Configuration: Configure publishing logic through UI-driven experiences without requiring custom code or complex infrastructure.* Real-time Event Emission: Continuously publish Business Events from live data streams, enabling downstream consumers to react instantly.* Integrated Publisher Lifecycle: Manage, update, and monitor Eventstream publishers alongside other Business Event producers in a unified experience.","feature_name":"Eventstream as a Business Events Publisher from Real-Time Hub","last_modified":"2026-07-17","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"Q3 2026","release_item_id":"c8df0463-d93d-f111-88b5-6045bd0a8ec1","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Expanding Real-Time Intelligence data sources with Cribl source (Preview)","blog_url":"https://blog.fabric.microsoft.com/en-US/blog/expanding-real-time-intelligence-data-sources-with-cribl-source-preview","feature_description":"The Cribl source for Eventstream allows you to stream data from Cribl Stream into Fabric Eventstream. You can add Cribl as a source to your eventstream to capture, transform, and route real-time events to various destinations in Fabric.","feature_name":"Eventstream connector: Cribl","last_modified":"2026-07-17","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"Q3 2026","release_item_id":"4ec67f5b-2c82-f111-ab0f-002248085b3f","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Introducing Enhanced Capabilities in Eventstream: Derived Streams, Edit modes, and Smart Routing","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/introducing-enhanced-capabilities-in-eventstream-derived-streams-edit-modes-and-smart-routing","feature_description":"**Eventstream as a Business Events Consumer from Real-Time Hub****Overview**Enable Eventstream to natively consume Business Events from Real-Time hub--unlocking real-time processing, transformation, and routing of business signals across Fabric.**Key Capabilities*** Native Eventstream Consumption: Create Eventstream consumers directly from Business Events in Real-Time hub to start processing events in real time.* Real-time Event Processing Pipelines: Build pipelines to ingest, process, and route Business Events with low latency as they are published.* Flexible Transformations & Enrichment: Apply transformations, filtering, and enrichment to Business Events before routing them to downstream systems.* Seamless Routing to Destinations: Deliver processed events to multiple Fabric destinations (e.g., storage, analytics engines, downstream services) for further action.* Integration with Analytics & Actions: Enable scenarios such as real-time insights, anomaly detection, alerts, and automated workflows powered by event data.","feature_name":"Eventstream as a Business Events Consumer from Real-Time Hub","last_modified":"2026-07-17","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"Q3 2026","release_item_id":"452a8734-da3d-f111-88b5-6045bd0a8ec1","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Acquiring Real-Time Data from New Sources with Enhanced Eventstream","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/acquiring-real-time-data-from-new-sources-with-enhanced-eventstream","feature_description":"**Business Events as a Source in Eventstream****Overview**Enable Business Events in Real-Time hub to be used as a native source in Eventstream--allowing users to easily ingest business signals into streaming pipelines for real-time processing and analytics.**Key Capabilities*** Native Business Events Source Integration: Select Business Events directly from Real-Time hub as an input source when creating Eventstream pipelines.* Seamless Event Subscription: Subscribe to relevant Business Events and stream them into Eventstream without requiring custom connectors or ingestion logic.* Unified Streaming Pipeline Creation: Combine Business Events with other data streams in a single Eventstream pipeline for richer, context-aware processing.* Real-time Event Ingestion: Continuously ingest Business Events as they are published, enabling low-latency processing and downstream actions.* Flexible Processing & Routing: Apply transformations, filtering, and routing logic within Eventstream to drive analytics, storage, or automated workflows.","feature_name":"Business Events as a Source in Eventstream","last_modified":"2026-07-17","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"Q3 2026","release_item_id":"0ba4b543-d83d-f111-88b5-6045bd0a8ec1","release_status":"Planned","release_type":"Public preview"}],"links":{"first":"/api/releases?page_size=50&page=1","last":"/api/releases?page_size=50&page=22","next":"/api/releases?page_size=50&page=2","prev":null,"self":"/api/releases?page_size=50&page=1"},"pagination":{"has_next":true,"has_prev":false,"next_page":2,"page":1,"page_size":50,"prev_page":null,"total_items":1070,"total_pages":22}}