{"data":[{"active":true,"blog_title":"What\u2019s new in Fabric Eventstream: July\u2013December 2025 updates","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/what%E2%80%99s-new-in-fabric-eventstream-july%E2%80%93december-2025-updates/5172334","feature_description":"### Large Message Support in Fabric Eventstreams  #### Overview  Large Message Support enables Fabric Eventstreams to ingest, process, and deliver event payloads up to **20 MB** per message. This removes the need for customers to split, chunk, or pre-process large events before streaming them through Eventstreams.  #### Problem  Today, Eventstreams enforces a default message size limit of 1 MB. Customers with larger payloads -- such as batched IoT telemetry, enriched event schemas, media metadata, or complex domain events -- must implement workarounds to stay within this constraint. These workarounds add pipeline complexity, increase failure surface, and break downstream schema consistency.  #### What's changing  With this feature, customers can opt in to a configurable message size ceiling (up to 20 MB) on their Eventstreams. Key aspects:  - **Opt-in by design** -- existing Eventstreams are unaffected unless the customer explicitly enables a higher limit. - **Configurable ceiling** -- customers choose the maximum message size aligned to their workload, rather than a one-size-fits-all default. - **No pipeline redesign required** -- sources, transformations, and destinations continue to work as expected. Larger messages flow through the same Eventstream canvas.","feature_name":"Large message support ( 20 MB) for Fabric Event streams","last_modified":"2026-09-04","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"Q3 2026","release_item_id":"4dc5810b-2040-f111-88b5-6045bd0066ad","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"Subscribe to Fabric Job Events at the workspace scope to monitor job activity across an entire workspace.","feature_name":"Workspace scope for Job Events","last_modified":"2026-09-03","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"Q1 2027","release_item_id":"7c18efd5-e4c0-f011-bbd3-000d3a5b0efa","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Capacity Operation Events in Real-Time Hub (Preview)","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Capacity-Operation-Events-in-Real-Time-Hub-Preview/ba-p/5364092","feature_description":"Public preview of Capacity Operation events to track Fabric capacity operations such as scale, pause, and resume.","feature_name":"Capacity Operation Events","last_modified":"2026-09-03","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"Q3 2026","release_item_id":"dc8a0aee-e3c0-f011-bbd3-000d3a5b0efa","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Build agent with AI for Data Agent (Preview)","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Build-agent-with-AI-for-Data-Agent-Preview/ba-p/5351116","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":"2026-09-02","product_id":"951b64e0-a663-f111-a826-6045bd00f798","product_name":"Conversational Analytics","release_date":"Q4 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":"The primary goal is to enable creators to understand how consumers interact with their data agents and bridge the gap between the data agent creators and the consumers. This comprehensive monitoring system will enable creators to have access to the feedback provided by consumer across consumption channels such as M365 Copilot, Fabric, Org App so they can refine the data agent based on customer feedback.Our initial release will only cover the M365 Copilot and will then expand to other consumption channels.","feature_name":"Consumer to Creator feedback loop","last_modified":"2026-09-02","product_id":"951b64e0-a663-f111-a826-6045bd00f798","product_name":"Conversational Analytics","release_date":"Q4 2026","release_item_id":"5db8bce1-2b43-f111-88b5-6045bd00fc61","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Build agent with AI for Data Agent (Preview)","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Build-agent-with-AI-for-Data-Agent-Preview/ba-p/5351116","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":"2026-09-02","product_id":"951b64e0-a663-f111-a826-6045bd00f798","product_name":"Conversational Analytics","release_date":"Q4 2026","release_item_id":"54678565-fb68-f111-a826-6045bd0066ad","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Fabric SQL Database Integration: Unlocking New Possibilities with Power BI desktop","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/fabric-sql-database-integration-unlocking-new-possibilities-with-power-bi-deskto/5172797","feature_description":"Connect directly from Power Query in Excel to any Fabric item exposing a SQL analytics endpoint -- Lakehouse, Warehouse, mirrored databases -- through a single Get Data entry point, with no need to pick the right artifact-specific connector.","feature_name":"Power Query - Excel connectivity to Fabric artifacts via SQL analytics endpoint","last_modified":"2026-09-01","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q4 2026","release_item_id":"995c4995-98a5-f111-b8dd-6045bd0196b9","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Outbound access protection for Data Factory (Generally Available)","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/outbound-access-protection-for-data-factory-generally-available/5172006","feature_description":"Outbound access protection is supported by dbt job items. When OAP is enabled on a workspace, dbt jobs run under the workspace's outbound access policy -- outbound network calls from the dbt job are blocked unless explicitly allowed.Preview limitation: Public packages are not supported during preview. If OAP is enabled on a workspace containing a dbt job, package resolution from public sources will fail. Support for public packages will be added by GA.","feature_name":"Outbound Access Protection for dbt job","last_modified":"2026-09-01","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"e7abebd0-9aa5-f111-b8dd-6045bd0194ef","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"The Reference Data Join feature in Eventstream enables you to enrich streaming events by joining them with static or slowly changing delta tables in one lake.User can reference any one lake delta tables using lakehouse, and join it with streaming data in eventstream to enahace and add additional context to their events during processing.","feature_name":"Eventstream reference data join with one lake delta tables","last_modified":"2026-08-28","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"Q3 2026","release_item_id":"816ee551-33a5-f011-bbd3-000d3a30273e","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Fabric Eventstream can now publish Business Events (Preview)","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Fabric-Eventstream-can-now-publish-Business-Events-Preview/ba-p/5180050","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":"2026-08-28","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":"Public preview"},{"active":true,"blog_title":"Turn insights into signals with Activator as a Business Event Publisher (Preview)","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Turn-insights-into-signals-with-Activator-as-a-Business-Event/ba-p/5190248","feature_description":"","feature_name":"Activator as a publisher of Business Events","last_modified":"2026-08-28","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"Q2 2026","release_item_id":"ffd42d88-e631-f111-88b3-000d3a376c0f","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Analyze Your Business Events in Eventhouse and Real-Time Dashboards (Preview)","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Analyze-Your-Business-Events-in-Eventhouse-and-Real-Time/ba-p/5190245","feature_description":"Route and analyze Fabric Business Events in Eventhouse for real-time querying and analytics.","feature_name":"Analyze Business Events in Eventhouse","last_modified":"2026-08-28","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"Q2 2026","release_item_id":"a1bbb828-e731-f111-88b3-000d3a376c0f","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Copilot in Fabric (preview) is available worldwide","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/copilot-in-fabric-preview-is-available-worldwide%C2%A0/5172049","feature_description":"Creators in Fabric can easily improve their semantic models with Copilot. Powered by Power BI Modeling MCP, you will be able to leverage AI to modify semantic model to meet new requirements for reporting, including but optiming your semantic model for AI.","feature_name":"Modeling Copilot","last_modified":"2026-08-28","product_id":"642a8375-05fc-ee11-a1ff-000d3a341a60","product_name":"Power BI","release_date":"Q2 2026","release_item_id":"18e80601-8e2c-f111-88b4-000d3a376137","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Securing the Power Query connector ecosystem in Fabric","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Securing-the-Power-Query-connector-ecosystem-in-Fabric/ba-p/5195164","feature_description":"Microsoft Fabric will introduce a modernized replacement for the existing Exchange connector, improving the foundation for connecting to Exchange data through Power Query-based experiences.","feature_name":"Connectors - Modernized Exchange connector for Power Query","last_modified":"2026-08-26","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q4 2026","release_item_id":"fbf626c8-726a-f111-a826-000d3a36696c","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"Adding filter pane cards to the visual itself via new filter action button. The filter action button co-exists with the visual title section.","feature_name":"Matrix and table filter action button","last_modified":"2026-08-26","product_id":"642a8375-05fc-ee11-a1ff-000d3a341a60","product_name":"Power BI","release_date":"Q4 2026","release_item_id":"ed888d13-03a1-f111-b8db-000d3a3ad657","release_status":"Planned","release_type":"Public preview"},{"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":"2026-08-26","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"Q4 2026","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":"A commonly requested new capability for Output Destinations is the ability to merge, or upsert, data into previously loaded rows in the destination table. We aim to provide this support for Fabric Lakehouse destination.","feature_name":"Dataflows - Merge/Upsert Support for Output Destinations","last_modified":"2026-08-26","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q4 2026","release_item_id":"67d0f235-4521-f011-9989-6045bd030c4d","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"New Dataflow Gen2 data destinations and experience improvements","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/new-dataflow-gen2-data-destinations-and-experience-improvements/5172568","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":"2026-08-26","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q4 2026","release_item_id":"45a4fc04-9ab3-f011-bbd3-000d3a30273e","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"A new core visual in Power BI -- the gantt chart, visualizing all your product tasks in a Power BI report.","feature_name":"Gantt chart visual","last_modified":"2026-08-26","product_id":"642a8375-05fc-ee11-a1ff-000d3a341a60","product_name":"Power BI","release_date":"Q4 2026","release_item_id":"15b7fc3d-d31f-f111-8341-6045bd0a8ec1","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"A wave of new Dataflow Gen2 capabilities at FabCon Atlanta 2026","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/a-wave-of-new-dataflow-gen2-capabilities-at-fabcon-atlanta-2026/5172175","feature_description":"We are introducing Google BigQuery as a new data destination for Dataflow Gen2 in Preview, enabling customers to publish transformed data from Microsoft Fabric directly into BigQuery tables using Dataflow Gen2's low-code Power Query experience.This preview expands Dataflow Gen2's destination ecosystem to better support multi-cloud analytics architectures, giving customers with existing investments in Google Cloud a simple way to deliver Fabric-based transformations into their BigQuery data warehouse -- without building or maintaining custom pipelines and orchestration.Key benefits and scenarios:* Publish curated outputs from Dataflow Gen2 directly into Google BigQuery datasets and tables* Support multi-cloud analytics and data sharing scenarios while keeping transformation logic centralized in Fabric* Prepare, standardize, and reshape data in Fabric before making it available to BigQuery-based reporting, machine learning, and downstream processing* Complete the round trip for BigQuery customers, complementing existing read connectivity and mirroring with a first-class write path* Reduce operational overhead by replacing hand-built export and load jobs with a governed, refreshable dataflowDuring Preview, the Google BigQuery data destination is intended for evaluation and feedback, allowing customers to validate connectivity patterns, load performance, 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 Microsoft Fabric.","feature_name":"Dataflows - New Destination: Google BigQuery","last_modified":"2026-08-26","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"8ea01c69-98a1-f111-b8db-0022480b837b","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Evaluate Fabric workload performance with the modern evaluation engine for VNet data gateways (Preview)","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Evaluate-Fabric-workload-performance-with-the-modern-evaluation/ba-p/5330293","feature_description":"We are introducing a .NET (NetCore) query evaluator for the VNET data gateway.The evaluator executes mashup queries; moving it to a modern .NET runtime modernizes how queries run. With this update:- Query evaluation on the VNET data gateway runs on a modern .NET runtime.- Performance, security, and long-term supportability improve.- The gateway is positioned to benefit from ongoing .NET investments.This reliability and performance investment is available initially as a preview.","feature_name":"Gateways - Performance improvement in VNet gateway through the modern .NetCore evaluator enablement","last_modified":"2026-08-26","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"6693ef43-ee7e-f111-ab0f-000d3a5a7aa2","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"Display a built-in trend chart (line or column) inside Card visuals to show how values change over time. No SVGs or extra visuals needed.","feature_name":"Card Visual - Trend Chart","last_modified":"2026-08-24","product_id":"642a8375-05fc-ee11-a1ff-000d3a341a60","product_name":"Power BI","release_date":"Q4 2026","release_item_id":"df66c8a5-d46f-f111-ab0d-000d3a376137","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"New capabilities focused on making T-SQL more powerful for analytics, simpler to write, and more intuitive for data exploration.<br/>* **Advanced Analytics** Expanded statistical capabilities with functions such as `MEDIAN`, `QUANTILE`, `PERCENTILE`, `APPROX_MEDIAN`, and `APPROX_QUANTILE` for distribution analysis and large-scale analytical workloads.* **Simplified Query Syntax** New language constructs including `QUALIFY`, `GROUP BY ALL`, and `ORDER BY ALL` help reduce query complexity and improve readability of analytical queries.* **More Intuitive Querying** Developer-friendly syntax enhancements designed to make common query patterns easier to express, improving productivity and reducing the amount of code required to answer business questions.","feature_name":"T-SQL language improvements","last_modified":"2026-08-24","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"Q4 2026","release_item_id":"ce1edca4-aa9f-f111-b8db-000d3a3ad8c1","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"Fabric Data Warehouse will introduce T-SQL functions for pattern matching, text extraction, and transformation using regular expressions. These capabilities will make it easier to validate, search, and manipulate text data directly within your queries.New functions include:* REGEXP_LIKE - Returns a Boolean indicating if the text matches the regex pattern.* REGEXP_REPLACE - Replaces occurrences of a regex pattern with a specified string.* REGEXP_SUBSTR - Extracts parts of a string based on a regex pattern, including Nth occurrence.* REGEXP_INSTR - Returns the position (start or end) of a matched substring.* REGEXP_COUNT - Counts how many times a regex pattern occurs in a string.","feature_name":"Regular expressions","last_modified":"2026-08-24","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"Q4 2026","release_item_id":"aceeae17-dfb8-f011-bbd3-000d3a30273e","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"What\u2019s New for Fabric Data Agents at Ignite 2025: Unlocking Deeper Data Reasoning and Seamless AI Interoperability","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/whats-new-for-fabric-data-agents-at-ignite-2025-unlocking-deeper-data-reasoning-/5172488","feature_description":"Fabric data agents empower customers to unlock insights from their enterprise data that lives in Fabric OneLake across different consumption channels.Microsoft Agent 365 (A365) is the enterprise control plane that IT and security teams use to observe, secure, and govern AI agents across the tenant, independent of where each agent was built. Admins manage and observe agents from the Microsoft 365 admin center.The first phase of Data Agent integration with A365 has two goals:* Data agent shows up in A365 with an enterprise identity (registration).* Publish activity telemetry to A365 (observability).","feature_name":"Data Agent A365 Identity and Observability","last_modified":"2026-08-24","product_id":"951b64e0-a663-f111-a826-6045bd00f798","product_name":"Conversational Analytics","release_date":"Q4 2026","release_item_id":"51ca432f-f39f-f111-b8db-000d3a5c7513","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"Recent NL2SQL improvements make the system more reliable and flexible by better leveraging few-shot examples, asking clarifying questions when queries are ambiguous, and using data exploration to handle gaps or novel scenarios. It also improves accuracy through smarter filter mapping and provides clearer debugging via visible run steps, example usage, and structured diagnostics.","feature_name":"Improved NL2SQL Experiences","last_modified":"2026-08-24","product_id":"951b64e0-a663-f111-a826-6045bd00f798","product_name":"Conversational Analytics","release_date":"Q2 2026","release_item_id":"7d8ccddd-a1f0-f011-8406-6045bd026004","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Build agent with AI for Data Agent (Preview)","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Build-agent-with-AI-for-Data-Agent-Preview/ba-p/5351116","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":"2026-08-24","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":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"Change your Ontology with confidence. Every saved version is listed newest-first with its timestamp, author, and description, so teams building an Ontology together can always see what changed, who changed it, and roll back to any earlier state.","feature_name":"Ontology Versioning","last_modified":"2026-08-21","product_id":"cef5a30d-562f-f011-8c4d-6045bd096d8f","product_name":"IQ","release_date":"Q3 2026","release_item_id":"e52b1f9e-3e40-f111-88b4-000d3a308b7b","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"Bring your business metrics into the Ontology. Reference measures that already live in a semantic model so they become part of the Ontology's model of your business -- carrying their name, definition, owner, and calculation logic with them. Because the metric points back to its source rather than copying it, agents and APIs querying the Ontology resolve the same live, trusted calculation your reports use.","feature_name":"Ontology Metrics Authoring","last_modified":"2026-08-21","product_id":"cef5a30d-562f-f011-8c4d-6045bd096d8f","product_name":"IQ","release_date":"Q3 2026","release_item_id":"cd2b1f9e-3e40-f111-88b4-000d3a308b7b","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Capacity Overview Events in Real-Time Hub (Generally Available)","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Capacity-Overview-Events-in-Real-Time-Hub-Generally-Available/ba-p/5356729","feature_description":"Public preview of Capacity Overview events to monitor Fabric capacity state changes through Azure and Fabric Events.","feature_name":"Capacity Overview Events","last_modified":"2026-08-21","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"Q4 2025","release_item_id":"26450555-49b3-f011-bbd2-0022480a2ecf","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"OneLake security (Generally Available)","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/OneLake-security-Generally-Available/ba-p/5176756","feature_description":"OneLake security is adding support for dynamic and multi-table row level security. With dynamic RLS, users can create roles in OneLake that secure data differently for each user that queries the data. In addition, multi-table RLS allows data to be secured by querying values in a lookup table. Combined, these features empower users to complex RLS rules for securing enterprise data.","feature_name":"OneLake security - dynamic and multi-table RLS","last_modified":"2026-08-20","product_id":"338c69fe-dcd6-ee11-9079-000d3a310f67","product_name":"OneLake","release_date":"Q1 2027","release_item_id":"dd026247-931f-f011-998a-0022480939f0","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Data Factory Adds CI/CD to Fabric Data Pipelines","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/data-factory-adds-cicd-to-fabric-data-pipelines/5173239","feature_description":"SQL Analytics Endpoint  supports Git integration and deployment pipelines in Fabric. 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Customers offset dates to compare a period against the same period a year earlier, to align a transaction date with the fiscal calendar their business reports on, to correct for a source system that records timestamps in a different time zone, or to account for a known lag between when an event happened and when it was captured. Each of these previously meant a custom column and M that had to handle month lengths and type conversions correctly. 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With backups in place, customers can look back at a previous run to understand when and where their data changed, compare a surprising result against the last known good snapshot before escalating it, and satisfy audit or record-keeping requirements that call for evidence of what was produced and when. Because snapshots land in a destination the customer already owns, they can be queried alongside the rest of their data, connected to downstream reports, or shared with colleagues who don't have access to the dataflow itself. 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