{"data":[{"active":true,"blog_title":"Fabric Runtime 2.0 (Preview)","blog_url":"https://blog.fabric.microsoft.com/en-US/blog/fabric-runtime-2-0-preview","feature_description":"Microsoft Fabric Runtime 2.1 updates the Apache Spark foundation to Spark 4.2 and Delta Lake 4.4, with Python 3.14, Scala 2.13, and Java 21. For supported workloads, the Native Execution Engine is enabled by default to provide acceleration without extra configuration, while documented opt-out and controlled fallback paths preserve compatibility.Runtime 2.1 also introduces a leaner built-in package baseline with PySpark and SparkR. Customers can add compatible optional Python and R libraries through Library Manager or a Fabric Environment, reducing package conflicts while preserving flexibility for data engineering and data science workloads. The Public Preview is targeted for January 31, 2027 and includes migration guidance, known limitations, and preview support information.","feature_name":"Preview - Runtime 2.1 (Spark 4.2 and Delta 4.4)","last_modified":"2026-10-15","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q1 2027","release_item_id":"a3c892bc-d7b2-f111-aaab-70a8a59cbac4","release_status":"Planned","release_type":"Public preview"},{"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":"2026-10-15","product_id":"0522b590-dcd6-ee11-9079-000d3a310f67","product_name":"Data Science","release_date":"Q1 2027","release_item_id":"75004e12-2158-f111-bec7-6045bd00f798","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":"Microsoft Fabric's outbound access protection blocks all outbound connections from a workspace by default and only allows approved external connections through managed private endpoints or data connection rules. It applies across workloads such as Data Engineering, Data Factory, Activator, and Data Agent, giving admins granular control over external connectivity.","feature_name":"Outbound Access Protection for Fabric SQL DB","last_modified":"2026-10-15","product_id":"347da228-ea54-ef11-a317-0022480a694f","product_name":"SQL database","release_date":"Q1 2027","release_item_id":"61e4ab87-eeb1-f111-aaab-70a8a59cbac4","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":"Data Agent monitoring - Consumer to Creator feedback loop","last_modified":"2026-10-15","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":"What\u2019s next for Fabric IQ Ontology: The operational context that powers your AI agents (Preview)","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/whats-next-for-fabric-iq-ontology-the-operational-context-that-powers-your-ai-ag/5172145","feature_description":"General availability of the Microsoft Fabric IQ Ontology item enables teams to maintain a shared model of business entities, properties, and relationships in Fabric. Modelers can connect those definitions to supported data sources and provide business context to supported analytics and agent experiences. The release covers the ontology item and its supported capabilities; individual integrations, security features, and advanced modeling capabilities retain their own release milestones.","feature_name":"Ontology item GA","last_modified":"2026-10-14","product_id":"cef5a30d-562f-f011-8c4d-6045bd096d8f","product_name":"IQ","release_date":"Q1 2027","release_item_id":"2a543dc3-74db-f011-8544-000d3a3740cc","release_status":"Planned","release_type":"General availability"},{"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":"2026-10-13","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q1 2027","release_item_id":"67d0f235-4521-f011-9989-6045bd030c4d","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Empowering Real-Time Searches: Vector Similarity Search with Eventhouse","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/empowering-real-time-searches-vector-similarity-search-with-eventhouse/5173156","feature_description":"# Generally Available: Vector Search and Vector Indexes in Azure SQL Vector Search and Vector Indexes are now generally available in Azure SQL Database. Using the [CREATE VECTOR INDEX](https://learn.microsoft.com/sql/t-sql/statements/create-vector-index-transact-sql?view=sql-server-ver17) and [VECTOR_SEARCH](https://learn.microsoft.com/sql/t-sql/functions/vector-search-transact-sql?view=sql-server-ver17) T-SQL capabilities, you can perform efficient approximate nearest neighbor (ANN) searches for AI scenarios such as semantic search, retrieval-augmented generation (RAG), recommendation systems, and image search. Built on the DiskANN algorithm, Vector Indexes enable efficient vector similarity search over large datasets while allowing you to combine vector search with relational queries and filtering directly in Azure SQL Database.By bringing AI retrieval directly into Azure SQL Database, you can build AI-powered applications on your enterprise data without requiring a separate vector database, simplifying application architecture while leveraging the reliability, security, and governance capabilities of Azure SQL Database.**Learn more:** [Vector Search & Vector Indexes documentation](https://learn.microsoft.com/sql/sql-server/ai/vectors?view=sql-server-ver17)","feature_name":"Vector Index & Search","last_modified":"2026-10-12","product_id":"347da228-ea54-ef11-a317-0022480a694f","product_name":"SQL database","release_date":"Q3 2026","release_item_id":"f52bbc7f-0fb1-f111-aaab-70a8a59cbbb7","release_status":"Shipped","release_type":"General availability"},{"active":true,"blog_title":"Zero-copy access to OneLake data in Azure Databricks (Preview)","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/zero-copy-access-to-onelake-data-in-azure-databricks-preview/5172291","feature_description":"Production-ready support for Azure Databricks to store data in OneLake with OneLake Security enabled","feature_name":"Databricks - Onelake as native storage - GA / Production-ready","last_modified":"2026-10-09","product_id":"338c69fe-dcd6-ee11-9079-000d3a310f67","product_name":"OneLake","release_date":"Q4 2026","release_item_id":"7d974ea6-c7c3-f111-aaaf-70a8a5b194a9","release_status":"Planned","release_type":"General availability"},{"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":"2026-10-08","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":"Shipped","release_type":"Public preview"},{"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://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/large-string-and-binary-values-in-fabric-data-warehouse-and-sql-analytics-endpoi/5172438","feature_description":"SQL analytics endpoints for Lakehouses will support large string and binary data using VARCHAR(MAX) and VARBINARY(MAX) types, up to 16 MB, with the new Metadata Sync feature.","feature_name":"VARCHAR(MAX) support in SQL analytics endpoint of Lakehouses (new metadata sync)","last_modified":"2026-10-08","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":"Shipped","release_type":"General availability"},{"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":"2026-10-08","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":"Shipped","release_type":"General availability"},{"active":true,"blog_title":"Expanded Data Agent Support for Large Data Sources","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/expanded-data-agent-support-for-large-data-sources/5172654","feature_description":"Production Datawarehouses, Lakehouses, Semantic Models, and SQL DBs contain hundreds to tens of thousands of Tables, Views, Functions, and Measures. This feature highlights the requirements to support production scale database systems and semantic models across data agent scenarios within the Data Agent UX seamlessly:  - Enabling the UX to allow users to load large schemas into Data Agent and select Objects easily and efficiently.- Enabling the UX to search for elements allowing users to easily identify key entities required for data agent input - Enabling pagination in the UX to seamlessly browse the schema in the Object explorer.","feature_name":"Explore Large Schemas in the Data Agent Schema Explorer","last_modified":"2026-10-08","product_id":"951b64e0-a663-f111-a826-6045bd00f798","product_name":"Conversational Analytics","release_date":"Q3 2026","release_item_id":"a6aadfd9-4924-f011-8c4d-00224804b6c3","release_status":"Shipped","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":"Microsoft Fabric Data Agent is improving its Ontology experience so customers can build agents that deliver more accurate, reliable, and verifiable answers grounded in their business concepts and relationships. With this release, customers should see better support for complex questions, clearer visibility into answer generation, stronger customization and scoping, and improved confidence as ontologies evolve over time.","feature_name":"Data Agent to Upgrade Ontology Integration","last_modified":"2026-10-08","product_id":"951b64e0-a663-f111-a826-6045bd00f798","product_name":"Conversational Analytics","release_date":"Q3 2026","release_item_id":"8640c298-fc68-f111-a826-6045bd0066ad","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Analyze Dataverse tables from Microsoft Fabric","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/analyze-dataverse-tables-from-microsoft-fabric/5173251","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 and API","feature_name":"Shortcuts in Fabric Data Warehouse (Public Preview)","last_modified":"2026-10-07","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"Q1 2027","release_item_id":"ce7f2f21-9421-f011-9989-000d3a302e4a","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Simplify Schema Changes in Fabric Data Warehouse with ALTER COLUMN (Preview)","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Simplify-Schema-Changes-in-Fabric-Data-Warehouse-with-ALTER/ba-p/5177593","feature_description":"ALTER COLUMN to non-nullable lets customers enforce "this column must always have a value" directly, in place, no more full table rebuilds just to tighten a constraint. It saves time, avoids error-prone workarounds (CTAS, drop/rename), and preserves table history. Combined with default-constraint support, existing rows get backfilled automatically, so the change is safe and non-breaking.","feature_name":"ALTER COLUMN (GA)","last_modified":"2026-10-07","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"Q1 2027","release_item_id":"c287226c-6ec2-f111-aaad-70a8a5b19412","release_status":"Planned","release_type":"General availability"},{"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":"2026-10-07","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q1 2027","release_item_id":"45a4fc04-9ab3-f011-bbd3-000d3a30273e","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Dataflow Gen2 and Power Query innovations at Microsoft Build: Low-code data transformation with standout scale, performance, and reuse","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Dataflow-Gen2-and-Power-Query-innovations-at-Microsoft-Build-Low/ba-p/5189028","feature_description":"We plan to introduce PowerPoint Presentations (PPTX) as a new data destination for Dataflow Gen2, enabling customers to turn data prepared in Microsoft Fabric into presentation files using the low-code Power Query experience.Designed for citizen users, this destination brings prepared data into a familiar tool for communicating business results. It helps teams move from data transformation to stakeholder presentations, making it easier to share results through the PowerPoint documents they already use. Key benefits and scenarios:* Create data-driven presentations: Generate PowerPoint files from Dataflow Gen2 results for business reviews, operational updates, and team briefings.* Reuse preparation logic: Build presentation outputs on existing queries and transformations rather than preparing the same data separately.* Reduce manual handoffs: Simplify the process of moving prepared data into presentations.* Work in a familiar format: Deliver PowerPoint documents that business users can open, review, and share with colleagues.During Preview, customers can evaluate PowerPoint document generation in their workflows and share feedback to help refine the experience.","feature_name":"Dataflows - New Destination: PowerPoint Presentations (PPTX)","last_modified":"2026-10-07","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q4 2026","release_item_id":"d1fca7f4-6fc2-f111-aaad-70a8a5b194d8","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 plan to introduce HTML Document as a new data destination for Dataflow Gen2, enabling customers to turn data prepared in Microsoft Fabric into shareable, browser-friendly documents using the low-code Power Query experience. Designed for citizen users, this destination meets business teams where they are today--with content they can open, read, and share in familiar tools. It bridges the gap between preparing data and communicating results, without requiring a separate reporting solution to make those results accessible.Key benefits and scenarios:* Make prepared data accessible: Deliver dataflow results as HTML documents that colleagues and stakeholders can view in a web browser.* Simplify everyday communication: Create data-driven business summaries and operational updates for teams that work primarily with documents.* Reduce repetitive work: Generate document outputs when the dataflow runs, rather than manually copying query results into separate documents. During Preview, customers can evaluate HTML document generation for their everyday workflows and provide feedback to help refine the experience.","feature_name":"Dataflows - New Destination: HTML Document","last_modified":"2026-10-07","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q4 2026","release_item_id":"9cc12116-6ec2-f111-aaad-70a8a5b194d8","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":"The PostgreSQL data destination in Dataflow Gen2 will become generally available, enabling customers to prepare and transform data in Microsoft Fabric and publish the results directly into PostgreSQL tables using the low-code Power Query experience. This release helps customers connect their Fabric data preparation workflows with existing PostgreSQL investments, making transformed data available for reporting, analytics, and downstream applications.Key benefits and scenarios:* Deliver curated data to PostgreSQL: Publish cleaned and standardized outputs directly into PostgreSQL tables. * Simplify recurring data delivery: Use dataflow refreshes to load transformed results, reducing the need for separate export-and-load jobs.* Complement existing read connectivity: Combine PostgreSQL source and destination support to prepare data in Fabric and deliver the results back to PostgreSQL. General Availability will make this destination an option for customers looking to bring their Fabric-to-PostgreSQL data preparation and loading workflows into production.","feature_name":"Dataflows - New Destination: PostgreSQL","last_modified":"2026-10-07","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q4 2026","release_item_id":"7a722b30-6dc2-f111-aaad-70a8a5b194d8","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"We are enhancing the previously released Send Email action in Dataflow Gen2 with rich HTML formatting, enabling customers to create more readable, data-driven emails with query results--such as tables--embedded directly in the message body.This enhancement meets business users where they already work: their inbox. Customers can combine formatted content with data-bound tables to communicate results clearly, without manually copying data into emails or requiring recipients to open a separate report to see the included results.Key benefits and scenarios:* Embed data in context: Include data-bound tables alongside explanatory text, keeping results and their business context together.* Improve readability: Use rich HTML formatting to create structured, easy-to-scan email updates.* Simplify business communication: Deliver data-driven operational summaries and status updates through the existing Send Email action.* Reduce manual preparation: Populate email content from query results rather than repeatedly assembling messages by hand.This enhancement will be available in Preview, giving customers an opportunity to try rich HTML email outputs and share feedback to help refine the experience.","feature_name":"Dataflows - Send Email with HTML Formatting","last_modified":"2026-10-07","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q4 2026","release_item_id":"2650603c-6fc2-f111-aaad-70a8a5b194d8","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Dataflow Gen2: Dataflow Diagnostics Download (Preview)","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/dataflow-gen2-dataflow-diagnostics-download-preview/5172149","feature_description":"We plan to make Diagnostics Download in Dataflow Gen2 generally available, providing a production-ready way to capture and share detailed runtime diagnostics for dataflow executions.Moving this feature to GA means it is ready for production usage, with consistent behavior, reliability, and support suitable for business-critical data pipelines.Key benefits and scenarios:* Download run-level diagnostic packages including execution details and runtime metadata* Speed up troubleshooting, root-cause analysis, and performance investigations* Enable easier collaboration between data teams, administrators, and Microsoft support using standardized diagnostic artifactsBy taking Diagnostics Download to GA, we're strengthening observability and supportability for Dataflow Gen2, making it easier to operate, troubleshoot, and trust dataflows at production scale.","feature_name":"Dataflows - Dataflow Diagnostics Download","last_modified":"2026-10-07","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"f98cc7d2-5834-f111-88b4-6045bd00f506","release_status":"Shipped","release_type":"General availability"},{"active":true,"blog_title":"From repetition to reuse: accelerate data prep with My queries in Dataflow Gen2","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/From-repetition-to-reuse-accelerate-data-prep-with-My-queries-in/ba-p/5176763","feature_description":"Shared Queries in Dataflow Gen2 will be available in Preview, enabling users to save commonly used Power Query transformations within a Fabric workspace, share and reuse them across multiple dataflows and collaborators.This preview extends the reuse-first authoring model to teams, making it easy to apply trusted, reusable logic--such as data cleansing steps, business rules, or standard joins--consistently across projects without rebuilding queries from scratch.Key benefits and scenarios:* Share common transformation logic across workspace members to accelerate team-wide development* Promote consistency and standardization of data preparation patterns across dataflows* Reduce duplication and improve maintainability by centralizing reusable query logic* Enable faster onboarding and collaboration when multiple authors work with similar source systemsDuring Preview, Shared Queries is intended for experimentation and early team adoption, helping organizations validate collaborative transformation patterns and provide feedback as we continue evolving the experience.","feature_name":"Dataflows - Shared Queries","last_modified":"2026-10-07","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"f8f739d4-b850-f111-bec7-6045bd0a8ec1","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Unlocking the Next Generation of Data Transformations with Dataflow Gen2 \u2013 FabCon Europe 2025 Announcements","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/unlocking-the-next-generation-of-data-transformations-with-dataflow-gen2---fabco/5172569","feature_description":"The option to disable V-Order (VertiParquet) compression in Dataflow Gen2 will become generally available, giving customers greater control over performance and storage optimization trade-offs for supported workloads and destinations.This capability enables advanced users to reduce processing overhead associated with V-Order compression during writes, helping improve refresh throughput and lower write latency for large-scale or time-sensitive ingestion and transformation scenarios.With this release, organizations can better tune Dataflow Gen2 execution behavior to align with the performance requirements of their Fabric architectures while continuing to benefit from the scalability and flexibility of Microsoft Fabric data transformation workflows.","feature_name":"Dataflows - Performance: Disable V-Order Compression","last_modified":"2026-10-07","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"eed6fd49-4461-f111-a826-000d3a376137","release_status":"Shipped","release_type":"General availability"},{"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":"Dataflow Gen2 refreshes appear in the Fabric Monitoring hub today, but the hub surfaces run-level status only. Investigating a failure requires leaving the hub and opening the dataflow's refresh history through Recent runs in the workspace. This feature brings the detailed refresh experience into the Monitoring hub, so users can move from a failed run to its root cause without changing context.Selecting a Dataflow Gen2 refresh entry in the Monitoring hub opens the detailed run view, with the same information available in Recent runs today: overall status, refresh type, duration, request and session identifiers, the tables loaded during the refresh, and the activities performed, including writes to output destinations. Users can drill into an individual table or activity to review errors and activity statistics such as rows and bytes written. Run entries also link directly to the dataflow and the workspace that produced them, so investigation and remediation happen in one place.This also completes the path that begins with a refresh failure notification. The link in a failure email lands the user on the run in the Monitoring hub, and from there they can navigate straight to the failure details rather than locating the dataflow manually.Key benefits and scenarios:* Investigate Dataflow Gen2 refresh failures directly in the Monitoring hub, without navigating back to the workspace to open Recent runs* Drill from a run into per-table and per-activity details, including error messages and output destination statistics* Navigate from a run entry to the originating dataflow and workspace* Monitor and troubleshoot Dataflow Gen2 alongside pipelines and other Fabric items in a single, consistent experienceBy bringing refresh details into the Monitoring hub, Dataflow Gen2 monitoring becomes consistent with the rest of Data Factory and gives data teams one place to detect, diagnose, and resolve refresh issues.","feature_name":"Dataflows - Refresh details in the Fabric Monitoring hub","last_modified":"2026-10-07","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"e3d18480-b4a8-f111-b8dd-6045bd019f33","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"Customers will be able to shift a date, datetime or time column backwards or forwards in time with a new Offset transformation, choosing the direction and the amount without writing custom logic.Shifting a date column is one of the most common preparation steps customers perform, and one of the fussiest to do by hand. 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. The Offset transformation applies the shift directly to the column, keeping its type intact and the resulting query readable to whoever maintains it next.","feature_name":"Power Query - Offset a date, datetime or time column","last_modified":"2026-10-07","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"c899806a-4e9b-f111-b8db-6045bd02b663","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"Customers will be able to insert the current date and time into a query with a single ribbon command. Today adds the current date, and Now adds the current date and time.This puts a common need within reach of customers who don't write M. Stamping rows with the moment a query ran is how customers record when data was loaded, tell one refresh apart from another, and build an audit trail that survives into the report. It is also the starting point for any calculation that is relative to the present: days since an order, records older than thirty days, whether a due date has passed. Until now these all began with hand-written M that customers had to look up, and a small syntax error at that first step would fail the whole query. Now and Today make the same starting point a click.","feature_name":"Power Query - Now and Today transforms","last_modified":"2026-10-07","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"b280a60f-4e9b-f111-b8db-6045bd02b663","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Dataflow Gen2 and Power Query innovations at Microsoft Build: Low-code data transformation with standout scale, performance, and reuse","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Dataflow-Gen2-and-Power-Query-innovations-at-Microsoft-Build-Low/ba-p/5189028","feature_description":"Power BI Desktop will adopt the Modern Power Query Editor, bringing next-generation authoring, usability, and productivity enhancements that are not available in the legacy Power Query experience. This modern editor aligns Power BI Desktop with the latest Power Query innovations used across Fabric, Dataflows, Power BI Web Modeling and other Power Query Online integrations, and serves as the foundation for ongoing transformation UX improvements.The modern editor introduces a set of authoring-centric capabilities designed to make complex queries easier to build, understand, and maintain--especially as data preparation workflows grow in size and sophistication.What's new in the modern Power Query editor:* Diagram View: Visualize query steps as a connected flow, helping you understand end-to-end transformation logic at a glance and more easily navigate complex, multi-step queries.* Schema View: Explore and manage columns through a dedicated schema-centric view, making it easier to understand table shape, reason about column-level changes, and prepare data with confidence.* Enhanced Steps pane: Clear step icons that distinguish different types of transformations. Query folding indicators that surface which steps are folding back to the source. Improved layout and interaction for reviewing and editing applied step.* Modern ribbon experience: A cleaner, more discoverable command surface--including support for a single-line ribbon--that scales better as new transformation capabilities are added.* Global search box: Quickly find commands, transformations, and actions across the editor without navigating menus or remembering exact locations.* Enhanced status bar: Richer, more contextual feedback during authoring and evaluation--making it easier to understand what the editor is doing and respond when issues arise.* Improved accessibility and keyboard navigation: Built to modern accessibility standards, enabling more efficient and inclusive data preparation workflows.Why this matters:* More productive authoring: Visual tools like Schema View, Diagram View, and enriched steps metadata reduce cognitive load when working with complex transformations.* Better transparency and trust: Folding indicators and clearer step semantics help users understand performance characteristics and execution behavior.* Future-ready foundation: The modern editor enables faster delivery of new Power Query features without being constrained by legacy UI architecture.This transition represents a major step forward in Power Query authoring for Power BI Desktop--unlocking modern capabilities that make data preparation more intuitive today, while ensuring customers benefit from continued innovation across Fabric and Power Query going forward.","feature_name":"Power Query - Modern Power Query Editor in Power BI Desktop","last_modified":"2026-10-07","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"a83463e5-6038-f111-88b5-6045bd0a8ec1","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"Power Query will introduce a new Sort Rows transformation dialog, delivering a clearer and more powerful experience for defining row ordering as part of data preparation.The new dialog makes it easy to sort data by one or multiple columns with explicit control over precedence and sort direction, all through a guided, visual interface. By centralizing sort logic into a dedicated dialog, Power Query helps users express intent more clearly and reduces the need to manage sorting through repetitive or hard-to-interpret steps.Why this matters:* Clearer sort intent: Define multi-column sorting and precedence in a single, easy-to-understand experience.* Improved usability: Reduce trial-and-error and make sorting more approachable for both self-service and advanced users.* Better maintainability: Sorting logic is easier to review, edit, and understand when revisiting or sharing queries.This enhancement continues Power Query's evolution toward a more modern, discoverable, and consistent transformation experience across Microsoft products, while supporting advanced scenarios that depend on well-defined row ordering.","feature_name":"Power Query - Sort Rows transformation dialog","last_modified":"2026-10-07","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"9f5f0427-5b38-f111-88b5-6045bd0a8ec1","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Wondering how to incrementally amass data in your data destination? This is how!","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/wondering-how-to-incrementally-amass-data-in-your-data-destination-this-is-how/5172281","feature_description":"Customers will be able to configure a backup to store a snapshot of a query's results after every dataflow run, in any of the supported data destinations. Each run writes a new, timestamped file or table into the destination the customer chose, so snapshots accumulate side by side instead of overwriting one another.This gives customers a durable record of what their data looked like at each point in time, which today they can only approximate by building a second dataflow, adding a downstream pipeline, or exporting results by hand. 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. Each snapshot is a byproduct of a run the customer is already performing, so the history builds itself with no extra artifacts to maintain and no manual steps to remember.","feature_name":"Dataflows - Backup data results across dataflow refreshes","last_modified":"2026-10-07","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"97d41049-3e9b-f111-b8db-6045bd02b663","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Recent data: Get back to your data faster in Fabric (Preview)","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/recent-data-get-back-to-your-data-faster-in-fabric-preview/5172293","feature_description":"We plan to make Recent Tables in Dataflow Gen2 generally available, helping users quickly reconnect to tables they've used before and accelerate the Modern Get Data experience.Going GA means Recent Tables is ready for production usage, with the reliability, consistency, and support required for everyday and business-critical data preparation workflows.Key benefits and scenarios:* Quickly re-access previously used tables without re-browsing or re-discovering sources* Faster iteration when building or updating multiple dataflows that reuse familiar data* A more efficient, streamlined Modern Get Data experience--especially in complex or shared environmentsBy bringing Recent Tables to GA, we're reducing friction in day-to-day authoring and making Dataflow Gen2 faster and more productive for repeat, production-scale data preparation.","feature_name":"Dataflows - Recent Tables","last_modified":"2026-10-07","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"9209ff64-9633-f111-88b4-6045bd0a886d","release_status":"Shipped","release_type":"General availability"},{"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":"Dataflows Gen2 will expand support for Dynamic Schema mode when writing to Fabric Data Warehouse destinations, making it easier to handle evolving data without manual rework.With Dynamic Schema, Dataflow Gen2 can automatically adapt to changes in your query schema--such as added or modified columns--when publishing data to a warehouse. This removes the need to pre-define and rigidly maintain table schemas as your data evolves.Why this matters:* Faster iteration: Update transformations without blocking on schema alignment.* Schema-flexible pipelines: Land evolving datasets into a warehouse with less friction.* Simpler migrations: Common Gen1 and self-service scenarios that rely on schema evolution become easier to move to Gen2.This capability is especially valuable for transformation-heavy pipelines and self-service data preparation, where schemas naturally change over time, and aligns Dataflow Gen2 more closely with the flexible, modern data engineering patterns supported across Microsoft Fabric.","feature_name":"Dataflows - Dynamic Schema Support for Fabric Data Warehouse Destination","last_modified":"2026-10-07","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"8ed33294-5a38-f111-88b5-6045bd00fc61","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"From repetition to reuse: accelerate data prep with My queries in Dataflow Gen2","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/From-repetition-to-reuse-accelerate-data-prep-with-My-queries-in/ba-p/5176763","feature_description":"We plan to make My Queries in Dataflow Gen2 generally available, enabling users to save commonly used Power Query transformations to a personal library and reuse them across multiple dataflows.Moving My Queries to GA means it is ready for production usage, with the reliability, consistency, and support required for standardizing data preparation at scale.Key benefits and scenarios:* Reuse proven transformation logic--such as cleansing steps, business rules, or standard joins--without rebuilding queries from scratch* Promote consistency and reduce duplication when building multiple dataflows against similar source systems* Make shared logic easier to maintain and evolve over time while accelerating the creation of new Dataflow Gen2 artifactsBy taking My Queries to GA, Dataflow Gen2 makes reuse, standardization, and long-term maintainability first-class capabilities for production-scale data preparation.","feature_name":"Dataflows - My Queries","last_modified":"2026-10-07","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"603ac87e-3135-f111-88b4-000d3a36696c","release_status":"Shipped","release_type":"General availability"},{"active":true,"blog_title":"Benchmarking Dataflow Gen2: Faster data transformation at lower cost","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Benchmarking-Dataflow-Gen2-Faster-data-transformation-at-lower/ba-p/5189072","feature_description":"The Optimized Lakehouse Writes capability for Dataflow Gen2 is now generally available, improving performance for a common pattern where data is processed using Staging Warehouse Compute and written to Lakehouse destinations, including scenarios such as persisting Computed Entity outputs.This enhancement reduces data movement overhead between Dataflow Gen2's Warehouse compute and Lakehouse data destinations, improving throughput and lowering end-to-end refresh times for large and complex dataflows. Customers benefit from faster data availability in their Lakehouses, more consistent refresh performance at scale, and improved efficiency when using warehouse-backed compute for intermediate transformation and shaping workloads.This capability is especially valuable for organizations standardizing on Lakehouse architectures and building scalable analytics and AI solutions on Microsoft Fabric.","feature_name":"Dataflows - Performance: Optimized Writes from Staging Warehouse Compute to Lakehouse Destinations","last_modified":"2026-10-07","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"57d40a9a-3335-f111-88b4-6045bd00fc61","release_status":"Shipped","release_type":"General availability"},{"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 plan to make the Snowflake Data Destination in Dataflow Gen2 generally available, enabling customers to publish curated, transformed data directly into Snowflake using Fabric's low-code data preparation experience.Moving this feature to GA means it is ready for production usage, with the reliability, consistency, and support expectations required for business-critical workloads.Key benefits and scenarios:* Publish standardized outputs from Dataflow Gen2 directly to Snowflake as part of governed Fabric workflows* Support hybrid and multi-cloud data architectures while keeping transformation logic centralized in Fabric* Enable teams to use Dataflow Gen2 as a production-grade ingestion and transformation layer for Snowflake-based analyticsBy taking the Snowflake Data Destination to GA, we're making it easier to operationalize end-to-end, production-scale data pipelines across Fabric and Snowflake with confidence.","feature_name":"Dataflows - New destination: Snowflake","last_modified":"2026-10-07","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"478cfce8-9633-f111-88b4-6045bd0a886d","release_status":"Shipped","release_type":"General availability"},{"active":true,"blog_title":"Quickly Connect to your Azure Resources in Fabric with the Data Pipeline Modern Get Data Experience","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/quickly-connect-to-your-azure-resources-in-fabric-with-the-data-pipeline-modern-/5173136","feature_description":"Customers will be able to browse and connect to their Azure resources directly from the modern get data experience in Power Query, without leaving the product to look up connection details. A new Azure module lists the resources the customer has access to across their subscriptions, and customers can filter by subscription, resource group and resource type, or search across the list by name, type and location. Selecting a resource creates or reuses a connection using the customer's organizational account and takes them straight to the data preview.This removes one of the most error-prone steps in connecting to Azure data. Today customers have to switch to the Azure portal, copy a server name, endpoint or URL, and paste it into Power Query by hand -- and for some sources the copied URL has to be edited before it will work, which is easy to get wrong and hard to diagnose. Because the Azure module only lists resources the customer already has access to, it also avoids the common failure of connecting to a resource the customer cannot actually read. For customers coming from Azure Data Factory, the browse-and-select pattern will feel familiar, and for everyone it turns a multi-step manual setup into a single click.","feature_name":"Power Query - Browse Azure resources in Get Data","last_modified":"2026-10-07","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"385d42a4-419b-f111-b8db-6045bd02b663","release_status":"Shipped","release_type":"General availability"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"The Send Email action in Dataflow Gen2 enables data-driven email notifications directly within a dataflow, allowing teams to send emails that reference and include query results produced during dataflow execution.Emails can dynamically incorporate snapshots of query evaluation results into the email body, as well as use query outputs as inputs to email parameters such as recipients, subject, importance, and message content. This makes it easy to deliver contextual, data-backed notifications without post-processing or custom scripting.The Send Email action can be triggered conditionally based on dataflow results, for example when a query returns rows, exceeds a threshold, or contains specific values. This enables common operational and analytical patterns such as exception alerts, data quality notifications, threshold-based reporting, and scheduled result sharing.By embedding this capability directly into Dataflow Gen2, customers can dramatically simplify end-to-end workflows that previously required stitching together multiple components--such as writing results to a destination, orchestrating execution with pipelines, and adding a separate email or automation step. With Send Email built in, Dataflow Gen2 can act as a self-contained, low-code data preparation and notification solution, supporting familiar scenarios previously achieved with dedicated workflow and analytics tools, while benefiting from native Fabric integration, governance, and scale.","feature_name":"Dataflows - Send Email Action","last_modified":"2026-10-07","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"35473b7a-e33c-f111-88b5-6045bd00fc61","release_status":"Shipped","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 plan to make the Excel Workbook (XLSX) Data Destination in Dataflow Gen2 generally available, enabling customers to publish curated, refreshed data directly into Excel files using Fabric's low-code transformation experience.Taking this feature to GA means it is ready for production usage, with the reliability, consistency, and support required for operational and business-critical workflows.Key benefits and scenarios:* Write transformed outputs from Dataflow Gen2 directly to Excel workbooks for broad accessibility* Enable business users to consume governed, regularly refreshed data in familiar Excel-based workflows* Support file-based sharing, automation, and downstream processes where Excel remains the system of recordBy bringing the Excel Workbook data destination to GA, Dataflow Gen2 becomes a production-ready bridge between governed Fabric transformations and everyday Excel-centric business processes.","feature_name":"Dataflows - New destination: Excel Workbooks (XLSX)","last_modified":"2026-10-07","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"2d795c03-a033-f111-88b4-6045bd0a886d","release_status":"Shipped","release_type":"General availability"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"The Excel Document Builder in Dataflow Gen2 provides a low-code experience for composing advanced Excel workbooks as the output of a dataflow. Generating formatted Excel documents is possible today through the advanced format of the Excel Workbook (XLSX) data destination, but it requires authoring the document structure directly in M. The Document Builder brings the same capability into a visual authoring dialog, enabling users to design multi-sheet workbooks without writing code.Users compose a workbook from a structure of sheets and parts, including Sheet Data, Table, Chart, and Range parts, and configure each part through a properties pane instead of M syntax. Charts can reference an existing table part by name rather than duplicating the underlying data, and built-in validation surfaces structural issues, such as an empty sheet or incompatible parts on the same sheet, before a refresh is consumed. On refresh, the dataflow renders the composed workbook and writes it to the configured destination.Key benefits and scenarios:* Design multi-sheet Excel workbooks with formatted tables and charts, without writing M code* Validate document structure during authoring, before the dataflow is refreshed* Deliver recurring, formatted Excel reports to business users on a refresh schedule, rather than raw data dumps* Preserve the code-first path: the Document Builder generates standard M, so document definitions remain versionable in source control and editable in the advanced editorBy adding a low-code authoring experience on top of the Excel Workbook data destination, Dataflow Gen2 makes advanced Excel document generation accessible to every dataflow author, not only those comfortable authoring M.","feature_name":"Dataflows - Excel Document Builder for the Excel Workbook (XLSX) destination","last_modified":"2026-10-07","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"040fd657-b2a8-f111-b8dd-6045bd019dcf","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Recent data: Get back to your data faster in Fabric (Preview)","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/recent-data-get-back-to-your-data-faster-in-fabric-preview/5172293","feature_description":"We plan to bring support for Recents to the Output Data experience, allowing users to easily reconnect to a recently used Output Destination, much like we have made available in the Get Data experience.","feature_name":"Dataflows - Output Destinations: Recents Support","last_modified":"2026-10-07","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"d1f84414-4621-f011-998a-000d3a341dd9","release_status":"Shipped","release_type":"General availability"},{"active":true,"blog_title":"Fabric IQ: The Semantic Foundation for Enterprise AI","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/fabric-iq-the-semantic-foundation-for-enterprise-ai/5172473","feature_description":"We are introducing Ontology Health in Microsoft Fabric to help modelers quickly assess whether an ontology is ready for real-world use. With this capability, ontology quality is continuously evaluated across three core areas--foundation, validation, and refinement--ensuring models are structurally sound, properly configured, and operationally ready. With this capability, you can: * Identify structural issues and inconsistencies through foundation checks to ensure the ontology is sound. * Verify required configurations using validation checks to confirm completeness and correctness. * Improve clarity, coverage, and usability with refinement checks for downstream consumption. * Quickly prioritize fixes with results categorized as Critical, Warning, or Information. * Navigate directly to impacted elements with guided actions, enabling faster issue resolution and increased trust in queries, graph experiences, and AI-powered outcomes.","feature_name":"Ontology Health","last_modified":"2026-10-06","product_id":"cef5a30d-562f-f011-8c4d-6045bd096d8f","product_name":"IQ","release_date":"Q4 2026","release_item_id":"8a059230-3855-f111-bec7-000d3a36696c","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Workspace-level private link for SQL database in Microsoft Fabric (Preview)","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Workspace-level-private-link-for-SQL-database-in-Microsoft/ba-p/5323519","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":"2026-10-06","product_id":"347da228-ea54-ef11-a317-0022480a694f","product_name":"SQL database","release_date":"Q4 2026","release_item_id":"744ac485-321d-f011-9989-000d3a302e4a","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Fabric CI/CD announcements- supporting new items and much more!","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/fabric-cicd-announcements--supporting-new-items-and-much-more/5173213","feature_description":"**Release:** Public Preview | **Product area:** Fabric CI/CD**Deployment plan** is a Fabric workspace item that lets you define the order in which items deploy and run automated actions before or after deployment.Use the visual canvas to:- Group the items you want to deploy and define dependencies between deployment groups- Add pre-deploy and post-deploy actions, such as running a Notebook, Data Pipeline, Copy Job, Dataflow Gen2, or User Data FunctionAttach a deployment plan when you use supported Git integration operations, deploy through a Deployment Pipeline, or call a supported REST API. During deployment, Fabric combines the plan's explicit dependencies with Fabric lineage to determine execution order. If an item or action fails, the operation stops without rolling back completed work.The result is a repeatable deployment flow that follows your intended order and runs the required actions around each item deployment.","feature_name":"Deployment Plan","last_modified":"2026-10-05","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":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Agentic Fabric: How MCP is turning your data platform into an AI-native operating system","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/agentic-fabric-how-mcp-is-turning-your-data-platform-into-an-ai-native-operating/5172009","feature_description":"Fabric Core MCP Server is a secure, cloud-hosted MCP endpoint that lets AI agents perform authenticated operations in Microsoft Fabric. It exposes Fabric public APIs as typed MCP tools for catalog search, workspace and item management, permissions, folders, capacity discovery, and long-running operations. Requests authenticate with Microsoft Entra ID, respect Fabric RBAC, and are recorded in Fabric audit logs. General availability removes the need for customers to install or operate a local server for these workflows.","feature_name":"Fabric Core MCP Server (GA)","last_modified":"2026-10-05","product_id":"c6da6b3b-ded6-ee11-9079-000d3a310f67","product_name":"Fabric Developer Experiences","release_date":"Q3 2026","release_item_id":"b682daa3-eeb0-f111-aaab-70a8a59cba8f","release_status":"Shipped","release_type":"General availability"},{"active":true,"blog_title":"Dataflow Gen2: Variable Library integration in Microsoft Fabric (Generally Available)","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/dataflow-gen2-variable-library-integration-in-microsoft-fabric-generally-availab/5172164","feature_description":"Eventstream now integrates with the Fabric Variable Library, so configurations can reference workspace variables. This makes Eventstream definitions portable across development, test, and production workspaces.With this capability, you can:- Reference workspace variables for connection identifiers and item references.- Reuse a single Eventstream definition across environments by swapping variable values.- Manage configuration centrally in the variable library instead of editing each item.","feature_name":"Variable Library support for Eventstream","last_modified":"2026-10-05","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"Q3 2026","release_item_id":"83351219-da49-f111-bec7-000d3a5b0273","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Monitor your Eventstreams with Workspace Monitoring \u2014 Now with per-Eventstream control (Preview)","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Monitor-your-Eventstreams-with-Workspace-Monitoring-Now-with-per/ba-p/5331185","feature_description":"Eventstream now writes operational logs to Fabric workspace monitoring, giving users visibility into stream health and behavior. This helps users detect issues, audit activity, and tune pipelines using familiar Fabric monitoring tools.With this capability, you can:- View Eventstream logs alongside other Eventstream metrics.- Investigate failures and delays with detailed operational records.- Track activity over time to support auditing and troubleshooting.","feature_name":"Workspace monitoring log support for Eventstream","last_modified":"2026-10-05","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"Q3 2026","release_item_id":"767f61ff-f13f-f111-88b5-6045bd00f798","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Workspace-Level Private Link in Microsoft Fabric (Generally Available)","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/workspace-level-private-link-in-microsoft-fabric-generally-available/5172560","feature_description":"Private Links enable secure, private connectivity between your virtual network and Microsoft Fabric, eliminating exposure to the public internet. With Eventstream's integration with Workspace Private Links, you gain granular access control at the workspace level enhancing data security by minimizing the risk of unauthorized access and potential data breaches.Now we are expanding the Workspace Private Links support to Eventstream Custom Endpoint, enabling secure inbound connection to Fabric.","feature_name":"Workspace Private Links for Eventstream Custom Endpoint","last_modified":"2026-10-05","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"Q3 2026","release_item_id":"53a8b940-263e-f111-88b5-6045bd0066ad","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Introducing New Branching Capabilities in Fabric Git Integration","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/introducing-new-branching-capabilities-in-fabric-git-integration/5173161","feature_description":"Branched workspaces introduce a redesigned developer experience for branch-out operations. Visual cues now show which workspaces are related, making it easier to navigate feature branches. New capabilities streamline the branch-out workflow and give developers more control over their feature workspaces.","feature_name":"Git Integration - Branched Workspaces","last_modified":"2026-10-05","product_id":"c6da6b3b-ded6-ee11-9079-000d3a310f67","product_name":"Fabric Developer Experiences","release_date":"Q3 2026","release_item_id":"50289248-632c-f111-88b4-6045bd0a886d","release_status":"Shipped","release_type":"General availability"},{"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":"2026-10-05","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":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Schema Registry: Creating type-safe pipelines using Schemas and Eventstreams (Preview)","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/schema-registry-creating-type-safe-pipelines-using-schemas-and-eventstreams-prev/5172612","feature_description":"Schema registry brings centralized schema management to Fabric Real-Time Intelligence, helping teams enforce data quality across streaming pipelines. It validates incoming events against registered schemas so downstream consumers receive consistent, well-formed data.With this capability, you can:- Register and manage schemas centrally for streaming data.- Validate events against schemas to enforce data quality.- Simplify integration across producers and consumers with shared schemas.","feature_name":"Schema registry for Eventstream","last_modified":"2026-10-05","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"Q3 2026","release_item_id":"123d1b43-24a6-f011-bbd3-000d3a5b0efa","release_status":"Shipped","release_type":"General availability"},{"active":true,"blog_title":"Expanding Real-Time Intelligence data sources with Cribl source (Preview)","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/expanding-real-time-intelligence-data-sources-with-cribl-source-preview/5172382","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":"2026-10-04","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":"Shipped","release_type":"General availability"}],"links":{"first":"/api/releases?page_size=50&page=1","last":"/api/releases?page_size=50&page=23","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":1123,"total_pages":23}}