{"data":[{"active":true,"blog_title":"AI-powered troubleshooting for Fabric pipeline error messages","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/ai-powered-troubleshooting-for-fabric-pipeline-error-messages/5172442","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":"2026-09-14","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q1 2027","release_item_id":"37499ba5-5b21-f011-9989-000d3a5b0147","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Simplifying Data Ingestion with Copy job \u2013 Introducing Change Data Capture (CDC) Support (Preview)","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/simplifying-data-ingestion-with-copy-job---introducing-change-data-capture-cdc-s/5172747","feature_description":"With the PostgreSQL CDC connector in Copy job, you can capture inserts, updates, and deletes from PostgreSQL and copy them to any supported destination.","feature_name":"Copy job - CDC based replication from PostgreSQL","last_modified":"2026-09-08","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q1 2027","release_item_id":"895d2725-473d-f111-88b5-002248085b3f","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Simplifying Data Ingestion with Copy job \u2013 Introducing Change Data Capture (CDC) Support (Preview)","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/simplifying-data-ingestion-with-copy-job---introducing-change-data-capture-cdc-s/5172747","feature_description":"With the MySQL CDC connector in Copy job, you can capture inserts, updates, and deletes from MySQL and copy them to any supported destination.","feature_name":"Copy job - CDC based replication from MySQL","last_modified":"2026-09-08","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q1 2027","release_item_id":"7f75427b-473d-f111-88b5-002248085b3f","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Simplify your data movement with Copy job: CDC with SQL estate (Generally Available)","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Simplify-your-data-movement-with-Copy-job-CDC-with-SQL-estate/ba-p/5184211","feature_description":"Copy job provides a Salesforce CDC connector, allowing you to capture row-level inserts, updates, and deletes from Salesforce and replicate them to any supported destination.","feature_name":"Copy job - CDC based replication from Salesforce","last_modified":"2026-09-08","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q1 2027","release_item_id":"46b875fc-433d-f111-88b5-002248085b3f","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"To enhance security, we aim to prevent the sharing of Microsoft account credentials across collaborators. The new connection-sharing model will encourage each collaborator to sign in with their own Microsoft account.","feature_name":"Connections - Enhanced Connection Collaboration Model","last_modified":"2026-09-08","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q4 2026","release_item_id":"f3579085-8c20-f011-998a-0022480939f0","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":"Mapping Data Flows transformations will become generally available in Dataflow Gen2, bringing the proven, low-code, Spark-based transformation capabilities of Azure Data Factory and Azure Synapse directly into Microsoft Fabric.With this release, customers will be able to confidently author and run complex, large-scale data transformations using the same visual, code-free experience they rely on today--natively integrated into the Fabric Dataflow Gen2 experience and ready for business-critical workloads.This capability will unlock the full power of Mapping Data Flows within Fabric, delivering Spark-optimized execution with predictable performance at scale. Data engineers and analytics teams will be able to leverage advanced transformation patterns while working within a unified Fabric Data Factory environment, reducing operational overhead and eliminating the need to manage separate tools.Just as importantly, general availability of Mapping Data Flows in Dataflow Gen2 will provide a production-ready migration path for existing Azure Data Factory and Synapse customers. Teams will be able to move their existing Mapping Data Flow assets into Fabric with minimal rework, preserving investments in transformation logic while modernizing and standardizing their data integration architecture on Microsoft Fabric.","feature_name":"Dataflows - Support for Mapping Data Flow transformations in Dataflow Gen2","last_modified":"2026-09-08","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q4 2026","release_item_id":"2f052713-3235-f111-88b4-6045bd006301","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 Amazon S3 as a new data destination for Dataflow Gen2 in Preview, enabling customers to publish transformed data from Microsoft Fabric directly into Amazon S3 using Dataflow Gen2's low-code Power Query experience.This preview expands Dataflow Gen2's destination ecosystem to better support multi-cloud data architectures, making it easier for customers with existing AWS investments to integrate Fabric-based transformations into their broader data lake and analytics environments.Key benefits and scenarios:* Publish curated outputs from Dataflow Gen2 directly to Amazon S3 buckets* Support AWS-based data lake and downstream analytics scenarios while keeping transformation logic centralized in Fabric* Enable teams to prepare, standardize, and shape data in Fabric before making it available to AWS services such as Athena, Glue, or other S3-based processing pipelinesDuring Preview, the Amazon S3 data destination is intended for evaluation and early adoption, allowing customers to validate cross-cloud integration patterns, performance characteristics, and operational workflows before using it for business-critical pipelines.This preview is part of our broader effort to make Dataflow Gen2 a flexible, low-code transformation layer across clouds, with continued investments planned to mature and expand multi-cloud destination support in Microsoft Fabric.","feature_name":"Dataflows - New Data Destination: AWS S3","last_modified":"2026-09-08","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q4 2026","release_item_id":"1f2fe2c0-3535-f111-88b3-000d3a376c0f","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Soft delete for\u00a0On-premises data\u00a0gateways in Microsoft Fabric (Preview)","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Soft-delete-for-On-premises-data-gateways-in-Microsoft-Fabric/ba-p/5365712","feature_description":"We are introducing user-facing soft delete for on-premises data gateways, giving administrators a safety net when gateways are removed.Accidental or premature deletion of a gateway can disrupt data refreshes and reports. With this update, administrators can:- Recover recently deleted gateways within a retention window.- Restore gateway configurations without recreating them from scratch.- Reduce the impact of accidental deletions on business-critical connectivity.This helps organizations protect against accidental loss of gateway configurations and maintain continuity.","feature_name":"Gateways - Soft delete for gateways","last_modified":"2026-09-08","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"d573bd24-ee7e-f111-ab0f-6045bd08179b","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Incremental copy gets more flexible: New watermark column types in Copy job in Fabric Data Factory (Generally Available)","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/incremental-copy-gets-more-flexible-new-watermark-column-types-in-copy-job-in-fa/5172147","feature_description":"Incremental copy from Copy job will support composite watermark columns for complex change detection, including scenarios where the maximum watermark is derived from multiple incremental columns such as LastCreateDateTime and LastModifiedDateTime together.","feature_name":"Copy job - Support composite watermark columns for incremental copy","last_modified":"2026-09-08","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"a42a4baf-483d-f111-88b5-002248085b3f","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Simplifying Data Ingestion with Copy job \u2013 Introducing Change Data Capture (CDC) Support (Preview)","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/simplifying-data-ingestion-with-copy-job---introducing-change-data-capture-cdc-s/5172747","feature_description":"Change Data Feed can be automatically enabled for Delta tables created by Copy job, allowing downstream workloads to directly leverage the Delta Change Data Feed to track all data changes.","feature_name":"Copy job - Auto-enable CDF on Delta table creation from Copy job","last_modified":"2026-09-08","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"30f3ad4a-423d-f111-88b5-002248085b3f","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Expanded CDC Support for More Sources & Destinations \u2013 Simplifying Data Ingestion with Copy job","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/expanded-cdc-support-for-more-sources--destinations---simplifying-data-ingestion/5172468","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":"2026-09-08","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"71ffb0dc-c99a-f011-b4cc-000d3a5b0efa","release_status":"Shipped","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":"2026-09-04","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q4 2026","release_item_id":"8ea01c69-98a1-f111-b8db-0022480b837b","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":"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":"2026-09-04","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":"Planned","release_type":"Public preview"},{"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":"2026-09-04","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":"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":"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":"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 Dataflow Gen1 - Gen2 Migration Wizard will become Generally Available and production-ready, providing a guided, end-to-end experience to help customers confidently upgrade existing Power BI Dataflows (Gen1) to Dataflow Gen2 in Microsoft Fabric.The wizard supports both single-item and bulk upgrades, enabling you to migrate one dataflow or multiple dataflows within a workspace through a single, streamlined flow. It is available for Dataflow Gen1 items in Premium workspaces and requires Fabric to be enabled for the upgraded Dataflow Gen2 items to run.Beyond upgrading the dataflow itself, the Migration Wizard automatically handles downstream dependencies, seamlessly redirecting references from Dataflow Gen1 outputs to the corresponding Dataflow Gen2 outputs--without requiring any manual changes to reports, semantic models, or other dependent items.With GA availability, this experience is fully supported for production use, dramatically simplifying Gen1 - Gen2 upgrade scenarios and making it safe and practical to bring existing Power BI Dataflows into Fabric to benefit from Dataflow Gen2's latest innovations, including improved performance, deeper Fabric integration, and a modern, future-ready Data Transformation foundation.","feature_name":"Dataflows - Dataflow Gen1 - Gen2 Migration Wizard","last_modified":"2026-08-26","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q4 2026","release_item_id":"98a73759-e13c-f111-88b5-6045bd00fc61","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":"2026-08-26","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":"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 improve the Google BigQuery connector experience by addressing query folding, data type handling, and connector reliability issues, enabling more consistent data access and transformation scenarios through Power Query-based experiences.","feature_name":"Connectors - Improved Google BigQuery connector reliability and query support","last_modified":"2026-08-26","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"befaeef2-736a-f111-a826-000d3a36696c","release_status":"Shipped","release_type":"General availability"},{"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":"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":"Partitioned Compute is a capability of Dataflow Gen2 that enables parts of a dataflow to run in parallel, reducing the time to finish its evaluations.Partitioned compute targets scenarios where the Dataflow engine can efficiently fold operations that can partition the data source and process each partition in parallel. For example, in a scenario where you're connecting to multiple files stored in an Azure Data Lake Storage Gen2, you can partition the list of files from your source, efficiently retrieve the partitioned list of files using query folding, use the combine files experience, and process all files in parallel.By moving to GA, Partitioned Compute becomes a stable, supported foundation for performant and highly scalable data transformation in Fabric.","feature_name":"Dataflows - Dataflows Gen2 Partitioned Compute","last_modified":"2026-08-18","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q4 2026","release_item_id":"385260b0-5b07-ef11-9f89-000d3a34b75c","release_status":"Planned","release_type":"General availability"},{"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":"2026-08-18","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":"Planned","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":"2026-08-18","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":"Planned","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":"2026-08-18","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":"Planned","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 Dataflow Gen1 - Gen2 Migration Wizard provides a guided, end-to-end experience to help customers upgrade existing Power BI Dataflows (Gen1) to Dataflow Gen2 in Microsoft Fabric with minimal effort.The wizard supports both single-item and bulk upgrades, allowing customers to migrate one dataflow or multiple dataflows within a workspace in a single flow. It is available for Dataflow Gen1 items in Premium workspaces and requires Fabric to be enabled to run the upgraded Dataflow Gen2 items.Beyond upgrading the dataflow itself, the Migration Wizard automatically handles downstream dependencies, seamlessly redirecting references from Dataflow Gen1 outputs to the new Dataflow Gen2 outputs--without requiring any manual changes to reports, semantic models, or other dependent items.This experience dramatically simplifies Gen1 - Gen2 migration scenarios, making it easy to bring existing Power BI Dataflows into Fabric and immediately benefit from Dataflow Gen2 innovations, including improved performance, deeper Fabric integration, and a modern Data Transformation foundation.","feature_name":"Dataflows - Dataflow Gen1 - Gen2 Migration Wizard","last_modified":"2026-08-18","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"5f7283fd-e03c-f111-88b5-6045bd00fc61","release_status":"Shipped","release_type":"Public preview"},{"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":"2026-08-18","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":"Planned","release_type":"General availability"},{"active":true,"blog_title":"dbt Job in Microsoft Fabric: Ship Trustworthy SQL Models Faster (Preview)","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/dbt-job-in-microsoft-fabric-ship-trustworthy-sql-models-faster-preview/5172453","feature_description":"We are adding support **dbt Fusion runtime** in dbt job in Microsoft Fabric as an execution environment, in addition to the existing dbt Core-based runtime.Customers can select the Fusion runtime when creating or configuring a dbt job to run their transformations on the next-generation dbt engine -- natively inside Fabric, with no local tooling or external orchestration to manage.","feature_name":"dbt job - Support for dbt Fusion runtime in dbt job","last_modified":"2026-08-12","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q4 2026","release_item_id":"11c809b6-6563-f111-a826-6045bd0a8ec1","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"New Features and Enhancements for Virtual Network Data Gateway","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/new-features-and-enhancements-for-virtual-network-data-gateway/5173049","feature_description":"For VNet data gateways, the fixed node setup can be inefficient during high-demand periods. To improve this, we're introducing auto-scaling, which dynamically adjusts the number of nodes based on demand.","feature_name":"Gateways - Autoscaling gateway clusters for VNet Gateway","last_modified":"2026-07-29","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"78dc21fc-7420-f011-998a-0022480939f0","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"New Dataflow Gen2 data destinations and experience improvements","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/new-dataflow-gen2-data-destinations-and-experience-improvements/5172568","feature_description":"We are introducing PostgreSQL as a new data destination for Dataflow Gen2, enabling customers to publish transformed data from Microsoft Fabric directly into PostgreSQL databases using Dataflow Gen2's low-code Power Query experience.This release expands Dataflow Gen2's destination ecosystem to better support operational, analytical, and application integration scenarios, giving customers a simple and flexible way to operationalize Fabric-based data transformations in PostgreSQL environments.Key benefits and scenarios:* Publish curated outputs from Dataflow Gen2 directly into PostgreSQL databases and tables* Support operational reporting, application integration, and downstream analytics scenarios using PostgreSQL as a serving layer* Enable teams to prepare, standardize, and reshape data in Fabric before delivering it to PostgreSQL-based applications, services, or analytics workloadsThe PostgreSQL data destination helps customers simplify data movement and reduce the need for custom pipelines by combining Fabric's scalable transformation capabilities with PostgreSQL's broad ecosystem compatibility and adoption.This release is part of our broader effort to make Dataflow Gen2 a flexible, low-code transformation layer across data platforms, with continued investments planned to expand destination support and simplify end-to-end data integration experiences in Fabric.","feature_name":"Dataflows - New Destination: PostgreSQL","last_modified":"2026-07-29","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"78a9449f-c85e-f111-a826-000d3a366c85","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Mirroring for Google BigQuery in Microsoft Fabric (Preview)","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/mirroring-for-google-bigquery-in-microsoft-fabric-preview/5172584","feature_description":"Mirroring for Google BigQuery enables customers to continuously replicate BigQuery data into Fabric OneLake, making it easier to use BigQuery data with Power BI, data engineering, data science, and AI experiences in Fabric. Customers can bring data from Google BigQuery into the Fabric analytics estate without maintaining separate ETL pipelines for common replication scenarios.","feature_name":"Mirroring - Google BigQuery","last_modified":"2026-07-23","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"e64919b9-5163-f111-a826-000d3a36696c","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Announcing the Availability of REST APIs for Connections and Gateways in Microsoft Fabric","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/announcing-the-availability-of-rest-apis-for-connections-and-gateways-in-microso/5173010","feature_description":"New tenant admin REST APIs for end-to-end visibility and lifecycle management of all cloud connections in Microsoft Fabric -- list, get, delete, manage permissions/ take over.","feature_name":"Connections - Tenant Admin APIs for connections","last_modified":"2026-07-21","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"79c8ba5e-fd3c-f111-88b5-6045bd00f798","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Modernize your ADF pipelines to unlock Fabric","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/modernize-your-adf-pipelines-to-unlock-fabric/5172081","feature_description":"In order to gain productivity boosts with AI in Fabric Data Factory, you can now easily upgrade your existing ADF factories to Fabric in a super easy friction-free way. Previously we had multi-step processes that were run from button clicks in ADF. Now your existing ADF factories can be automatically bridged from Fabric trial capacities and then easily upgraded as-is to native Fabric pipelines and data flows.","feature_name":"Pipelines - Simplified Upgrade Experience","last_modified":"2026-07-15","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"6c647278-de7f-f111-ab0f-6045bd00f798","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"When automating pipeline execution across complex data ecosystems, it is very important to only invoke those pipelines when specific requirements have first been met. In Fabric Data Factory, we refer to these as "pipeine dependencies". With this new feature, you will be able to set independent rules for each pipeline that requires data presences, trigger status, and pipeline status as optional depedency parameters for your pipeline runs.","feature_name":"Pipelines - Pipeline Dependencies","last_modified":"2026-07-13","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"6c71cf1a-007f-f111-ab0f-6045bd00f798","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Data Factory Increases Maximum Activities Per Pipeline to 80","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/data-factory-increases-maximum-activities-per-pipeline-to-80/5173237","feature_description":"With pipeline actions, we are extending the reach and re-usability of pipelines in Fabric Data Factory. Now you can take portions of your complex pipelines, a single activity, or even your entire pipeline, and register it as a Fabric Action. That Fabric Action is now fully accessible via endpoints in other pipelines, Data Activator, or UDFs.","feature_name":"Pipelines - Actions","last_modified":"2026-07-13","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"072d08ee-fd7e-f111-ab0f-6045bd00f798","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://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/run-spark-job-definitions-in-pipelines-with-service-principal-or-workspace-ident/5172396","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":"2026-07-13","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"99108f89-076f-f011-bec2-00224804b6c3","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Event-Driven Copy Job Execution with Fabric Activator (Generally Available)","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Event-Driven-Copy-Job-Execution-with-Fabric-Activator-Generally/ba-p/5195808","feature_description":"Fabric Activator can trigger Copy job executions in a native way, allowing event-driven scenarios without pipeline where a file landing in storage automatically initiates a Copy job.","feature_name":"Copy job - Activator support for triggering Copy jobs","last_modified":"2026-07-13","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q2 2026","release_item_id":"8943d03e-433d-f111-88b5-002248085b3f","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Running Apache Airflow jobs seamlessly in Microsoft Fabric","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/running-apache-airflow-jobs-seamlessly-in-microsoft-fabric/5172862","feature_description":"Microsoft Fabric Apache Airflow Jobs provided the easiest and quickest way to build enterprise-ready DAGs without any infrastructure requirements. They are based on open-source Apache Airflow and we have now updated our support to version 3.3 of Apache Airflow.","feature_name":"Airflow - Support for Apache Airflow 3.x","last_modified":"2026-07-12","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"e31334da-267e-f111-ab0f-6045bd00f798","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Running Apache Airflow jobs seamlessly in Microsoft Fabric","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/running-apache-airflow-jobs-seamlessly-in-microsoft-fabric/5172862","feature_description":"Network security is critical to running an effective and secured Apache Airflow environment. We are excited to bring Vnet and Private Link support to Apache Airflow jobs in Fabric Data Factory.","feature_name":"Airflow - Network Security","last_modified":"2026-07-08","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"46139b5a-c49d-f011-b41c-6045bd00f9db","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"AI-powered development with Copilot for Data pipeline \u2013 Boost your productivity in understanding and updating pipeline","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/ai-powered-development-with-copilot-for-data-pipeline---boost-your-productivity-/5172740","feature_description":"With pipeline Actions, you can now easily define a simple pipeline template that can be a single activity or multiple acitvities, without using the pipeline canvas. Simply tell Copilot what you want to achieve, answer a few configuration prompts, and then reuse your Actions in multiple pipelines or from other Fabric items like Data Activator or Functions.","feature_name":"Pipelines - Actions","last_modified":"2026-06-23","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"89223235-516f-f111-ab0d-6045bd006301","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"Data engineers encounter challenges when constructing metadata-driven workflows due to the need to establish multiple connections for diverse data source endpoints. Currently, existing connections lack the capability for dynamic referencing. To address this, we are introducing connections as a workspace item that will consolidate the connections Role Based Access Contro l(RBAC) with workspace. This feature will facilitate dynamic referencing in connections, thereby enhancing flexibility and efficiency in metadata-driven workflows.","feature_name":"Connections - Enabling customers to parameterize their connections","last_modified":"2026-06-16","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q4 2026","release_item_id":"cdc7d144-f9d6-ee11-9079-000d3a310f67","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":"2026-06-16","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":"Planned","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":"2026-06-16","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":"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":"A commonly used functionality in ADF is configuring a Pipeline to run when a set of dependencies are met. We are excited to bring this functionality to Data Pipelines in Data Factory in Fabric to continue enriching orchestration capabilities.","feature_name":"Pipelines - Run Pipeline when dependencies are met","last_modified":"2026-06-16","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"f8636911-7191-ef11-ac21-002248098a98","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Running Apache Airflow jobs seamlessly in Microsoft Fabric","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/running-apache-airflow-jobs-seamlessly-in-microsoft-fabric/5172862","feature_description":"Fabric Apache Airflow jobs provides a SaaS way to build Python-based Airflow DAGs in your Fabric workspace. With this new feature, you will now be able to easily view your runtime logs from Apache Airflow in the Fabric Workspace Monitoring view to build your own queries, reports, and dashboards.","feature_name":"Airflow - Workspace Monitoring to include Apache Airflow logs from Airflow Job","last_modified":"2026-06-16","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"ef14c40e-0a6f-f011-bec2-00224804b6c3","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Boost performance and save costs with Fast Copy in Dataflows Gen2","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/boost-performance-and-save-costs-with-fast-copy-in-dataflows-gen2/5173173","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":"2026-06-16","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":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Announcing Copy Job Activity in Data Factory Pipeline (Generally Available)","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/announcing-copy-job-activity-in-data-factory-pipeline-generally-available/5172435","feature_description":"Improve the read performance when reading a single large parquet file in Copy job and Pipeline activities (Copy activity, Lookup activity, GetMetadata activity, Delete activity) by allowing multi-threads.","feature_name":"Copy Job/Activity - Improve read performance for Parquet format when source is a single large file","last_modified":"2026-06-16","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"b918794e-059e-f011-b41c-00224808fcf0","release_status":"Planned","release_type":"General availability"},{"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":"2026-06-16","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":"Planned","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":"2026-06-16","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":"Planned","release_type":"Public preview"}],"links":{"first":"/api/releases?product_name=Data+Factory&page_size=50&page=1","last":"/api/releases?product_name=Data+Factory&page_size=50&page=5","next":"/api/releases?product_name=Data+Factory&page_size=50&page=2","prev":null,"self":"/api/releases?product_name=Data+Factory&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":243,"total_pages":5}}