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By embedding Fabric functions into SQL, teams can streamline operations and maintain consistency across platforms.For example, if you defined a `my_fabric_function` user data function, you cna call it from T-SQL code using the folowing T-SQL syntax:<br/>```SELECT *, my_fabric_function(param1, param2...) 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Simultaneously, users can ingest data from streaming sources into any CopyJob destination that supports incremental updates. This unified experience makes it easy to build hybrid data platforms that combine batch and streaming assets. As a result, creating event-driven and AI-powered applications--like dashboards, alerts, and compliance solutions--no longer requires manual data movement or complex integrations.","feature_name":"Unify batch & streaming data platforms with CopyJob & Real-Time Intelligence","last_modified":"2026-05-07","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"2026-09-30","release_item_id":"403b7b13-0ca4-f011-bbd3-000d3a5b0efa","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"AI-powered troubleshooting for Fabric pipeline error messages","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/ai-powered-troubleshooting-for-fabric-data-pipeline-error-messages","feature_description":"Copilot helps to troubleshoot Copy job error messages by providing clearer summary and actionable recommendations.","feature_name":"Copy job - AI-powered Error Assistant and Insight for Copy Job","last_modified":"2026-05-07","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"2026-09-30","release_item_id":"37499ba5-5b21-f011-9989-000d3a5b0147","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"New Dataflow Gen2 data destinations and experience improvements","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/new-dataflow-gen2-data-destinations-and-experience-improvements","feature_description":"We are introducing Google Cloud Storage (GCS) as a new data destination for Dataflow Gen2 in Preview, enabling customers to land transformed data from Microsoft Fabric directly into Google Cloud Storage using Dataflow Gen2's low-code Power Query experience.This preview expands Dataflow Gen2's destination ecosystem to better support multi-cloud data architectures, giving customers with existing investments in Google Cloud a simple way to integrate Fabric-based transformations into their broader data estate.Key benefits and scenarios:* Publish curated outputs from Dataflow Gen2 directly to Google Cloud Storage buckets* Support multi-cloud ingestion and data sharing scenarios while centralizing transformation logic in Fabric* Enable teams to prepare and standardize data in Fabric before making it available to GCP-based analytics, processing, or downstream pipelinesDuring Preview, the Google Cloud Storage data destination is intended for evaluation and feedback, allowing customers to validate connectivity patterns, performance characteristics, and integration workflows ahead of broader production use.This release is part of our broader effort to make Dataflow Gen2 a flexible, low-code transformation layer across clouds, with ongoing investments planned to further mature and expand multi-cloud destination support in Fabric.","feature_name":"Dataflows - New Destination: Google Cloud Storage","last_modified":"2026-05-07","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"2026-09-29","release_item_id":"45a4fc04-9ab3-f011-bbd3-000d3a30273e","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Announcing preview of Workspace Monitoring","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/announcing-public-preview-of-workspace-monitoring","feature_description":"We will enhance the Diagnostics experience for Apache Airflow jobs developers by surfacing logs in the Fabric Workspace Monitoring experience.","feature_name":"Airflow - Workspace logs integration for Diagnostics","last_modified":"2026-05-07","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"2026-09-29","release_item_id":"34a3246d-5921-f011-9989-000d3a329ecb","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Announcing preview of Workspace Monitoring","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/announcing-public-preview-of-workspace-monitoring","feature_description":"Before the feature release, the log data of a workspace can only be sent to the monitoring event house hosted in that workspace. This feature will allow ingesting log data of multiple workspaces to one monitoring event house. That will lower the cost, simplify the management and segregate monitoring solution from production environment.","feature_name":"Cross workspace monitoring","last_modified":"2026-05-07","product_id":"c6da6b3b-ded6-ee11-9079-000d3a310f67","product_name":"Fabric Developer Experiences","release_date":"2026-09-28","release_item_id":"a0744248-48b9-f011-bbd3-000d3a30273e","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"BULK INSERT in Fabric Data Warehouse","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/bulk-insert-statement-in-fabric-datawarehouse","feature_description":"Fabric Data Warehouse will support the bcp utility and the TDS Bulk Load API, enabling high-performance data ingestion from a variety of client tools such as bcp, SSIS, and Azure Data Factory. 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Centralized support for these APIs ensures consistency across ingestion pipelines and improves interoperability with existing tools.<br/>An example of a bcp command that loads file content into a DW table:<br/>```bcp dbo.artists in gold_artist.txt -d TextDW -c -S myworkspace.datawarehouse.fabric.microsoft.com -G -U theuser@microsoft.com ```","feature_name":"BCP","last_modified":"2026-05-07","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"2026-09-21","release_item_id":"d58f4693-ca80-ef11-ac21-6045bd062aa2","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"Adds a **date picker** mode for date slicers so authors can pick a relative default range (e.g., 'last full month') that automatically rolls forward as time passes when published, without authors having to update the report each period. Report viewers can override it to filter to other relative ranges or any manual date range.","feature_name":"Date picker slicer to set relative and manual date ranges","last_modified":"2026-05-07","product_id":"642a8375-05fc-ee11-a1ff-000d3a341a60","product_name":"Power BI","release_date":"2026-09-16","release_item_id":"48ffc4ba-fa13-f111-8406-002248085b3f","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":"2026-05-07","product_id":"642a8375-05fc-ee11-a1ff-000d3a341a60","product_name":"Power BI","release_date":"2026-09-15","release_item_id":"15b7fc3d-d31f-f111-8341-6045bd0a8ec1","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Run Spark Job Definitions in Pipelines with Service Principal or Workspace Identity","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/run-spark-job-definitions-in-pipelines-with-service-principal-or-workspace-identity","feature_description":"When automating your Fabric Data Factory pipelines, you will greatly benefit from having the flexibility to set the identity (i.e. user, SPN, workspace identity) of the pipeine at the time of execution. With this feature, you can now set it easily from the scheduler API and scheduler UI.","feature_name":"Pipelines - Set the \"Run As\" identity of the pipeline from the pipeline schedule","last_modified":"2026-05-07","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"2026-09-01","release_item_id":"99108f89-076f-f011-bec2-00224804b6c3","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Time-Travelling through data: The Magic of Table clones","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/time-travelling-through-data-the-magic-of-table-clones","feature_description":"Ability to clone warehouse across workspaces","feature_name":"Warehouse Clones","last_modified":"2026-05-07","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"2026-08-10","release_item_id":"e240d768-fc21-f011-998a-0022480939f0","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Simplifying data movement across multiple Clouds with richer CDC in Copy job in Fabric Data Factory Oracle source, Fabric Data Warehouse sink and SCD Type 2 (Preview)","blog_url":"https://blog.fabric.microsoft.com/en-US/blog/simplifying-data-movement-across-multiple-clouds-with-richer-cdc-in-copy-job-in-fabric-data-factory-oracle-source-fabric-data-warehouse-sink-and-scd-type-2-preview","feature_description":"Customers can use Copy job to automatically capture inserts, updates, and deletions from any supported CDC source store, and replicate them to the new destination stores including Oracle without requiring a watermark column.","feature_name":"Copy job - CDC based replication to Oracle","last_modified":"2026-05-07","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"2026-08-01","release_item_id":"71ffb0dc-c99a-f011-b4cc-000d3a5b0efa","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Fabric Spark Monitoring APIs (Generally Available)","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/general-availability-announcement-fabric-spark-monitoring-apis","feature_description":"This feature provides an integrated, real-time dashboard for monitoring Spark application performance at both the driver and executor levels. Users can visualize CPU, memory, and core utilization across running and completed Spark applications--whether triggered through interactive notebooks or batch jobs. The dashboard aligns with the Fabric SaaS experience and enhances visibility into Spark vCore allocation and utilization.Users can inspect performance metrics at any moment in the application lifecycle, analyze utilization patterns, and access recommended actions to address bottlenecks. In addition to real-time insights, the experience includes summaries of active jobs and tasks, detailed Spark compute configurations, and the ability to drill into the Spark UI, application history, job-level details, or code-level snapshots.","feature_name":"Fabric Spark Real-Time Performance Monitoring for CPU, Memory, and vCores","last_modified":"2026-05-07","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"2026-07-31","release_item_id":"9f72b035-86ba-f011-bbd3-6045bd00f9db","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":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":"2026-05-07","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"2026-07-30","release_item_id":"be34d186-0cb9-f011-bbd3-6045bd05dd14","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Data Warehouse Utilization Reporting in Fabric Capacity Metrics App","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/data-warehouse-utilization-reporting-in-fabric-capacity-metrics-app","feature_description":"Shortcuts in Fabric DW allow users to directly access and query data that is present in internal and external data sources, without loading them into Fabric DW. 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