{"data":[{"active":true,"blog_title":"Modernize your ADF pipelines to unlock Fabric","blog_url":"https://blog.fabric.microsoft.com/en-US/blog/modernize-your-adf-pipelines-to-unlock-fabric","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://blog.fabric.microsoft.com/en-us/blog/add-up-to-80-activities-to-your-pipeline","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://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":"2026-07-13","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://blog.fabric.microsoft.com/en-us/blog/running-apache-airflow-jobs-seamlessly-in-microsoft-fabric","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://blog.fabric.microsoft.com/en-us/blog/running-apache-airflow-jobs-seamlessly-in-microsoft-fabric","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":"Data Factory Increases Maximum Activities Per Pipeline to 80","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/add-up-to-80-activities-to-your-pipeline","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://blog.fabric.microsoft.com/en-US/blog/dataflow-gen2-dataflow-diagnostics-download-preview","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":"Create Metadata Driven Data Pipelines in Microsoft Fabric","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/create-metadata-driven-data-pipelines-in-microsoft-fabric","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":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":"2026-06-16","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"f3579085-8c20-f011-998a-0022480939f0","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Running Apache Airflow jobs seamlessly in Microsoft Fabric","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/running-apache-airflow-jobs-seamlessly-in-microsoft-fabric","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://blog.fabric.microsoft.com/en-us/blog/boost-performance-and-save-costs-with-fast-copy-in-dataflows-gen2","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":null,"blog_url":null,"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":"Incremental copy gets more flexible: New watermark column types in Copy job in Fabric Data Factory (Generally Available)","blog_url":"https://blog.fabric.microsoft.com/en-US/blog/incremental-copy-gets-more-flexible-new-watermark-column-types-in-copy-job-in-fabric-data-factory-generally-available","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":"2026-06-16","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":"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"},{"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":"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":"2026-06-16","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"98a73759-e13c-f111-88b5-6045bd00fc61","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Recent data: Get back to your data faster in Fabric (Preview)","blog_url":"https://blog.fabric.microsoft.com/en-US/blog/recent-data-get-back-to-your-data-faster-in-fabric-preview","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":"2026-06-16","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":"Planned","release_type":"General availability"},{"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":"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":"2026-06-16","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":"Planned","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://blog.fabric.microsoft.com/en-us/blog/simplifying-data-ingestion-with-copy-job-introducing-change-data-capture-cdc-support","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":"2026-06-16","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"895d2725-473d-f111-88b5-002248085b3f","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Simplifying Data Ingestion with Copy Job \u2013 Connection Parameterization, Expanded CDC and Connectors","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/simplifying-data-ingestion-with-copy-job-connection-parameterization-expanded-cdc-and-connectors","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":"2026-06-16","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"7f75427b-473d-f111-88b5-002248085b3f","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"New Features and Enhancements for Virtual Network Data Gateway","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/new-features-and-enhancements-for-virtual-network-data-gateway","feature_description":"For VNet data gateways, the fixed node setup can be inefficient during high-demand periods. To improve this, we're introducing auto-scaling, which dynamically adjusts the number of nodes based on demand.","feature_name":"Gateways - Autoscaling gateway clusters for VNet Gateway","last_modified":"2026-06-16","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":"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 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":"2026-06-16","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":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Use Fabric Data Factory Data Pipelines to Orchestrate Notebook-based Workflows","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/use-fabric-data-factory-data-pipelines-to-orchestrate-notebook-based-workflows","feature_description":"When building workflows with pipelines in Fabric Data Factory, it is very important to express rules that allow you to tell the workflow engine which conditions must be met before invoking or continuing your logic. In pipelines in Data Factory, we are super happy to announce that we've brought this capability into Fabric. If you are a user of ADF and Synpase, this feature was previously available as "trigger dependencies" inside of tumbling window triggers. With the inclusion now of dependencies inside of Fabric pipelines in Data Factory, you can now easily move your ADF and Synapse pipelines into Fabric.","feature_name":"Pipelines - Pipeline Dependencies","last_modified":"2026-06-16","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"754bc857-509d-f011-b41c-6045bd00f9db","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Simplifying Data Ingestion with Copy job \u2013 Copy data across tenants using Copy job in Fabric Data Factory","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/simplifying-data-ingestion-with-copy-job-copy-data-across-tenants-using-copy-job-in-fabric-data-factory","feature_description":"Customers will be able to work with real-time data by using Copy jobs to move data from Fabric Event Streams to supported destinations, or to copy data from supported sources into Fabric Event Streams as a destination.","feature_name":"Copy job - Copy data from/to Fabric Event Stream","last_modified":"2026-06-16","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"6d0c6c98-1b32-f111-88b4-000d3a376137","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Updates to default data destination behavior in Dataflow Gen2","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/updates-to-default-data-destination-behavior-dataflow-gen-2","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":"2026-06-16","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"67d0f235-4521-f011-9989-6045bd030c4d","release_status":"Planned","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":"2026-06-16","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":"Planned","release_type":"General availability"},{"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":"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":"2026-06-16","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":"Planned","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":"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":"2026-06-16","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":"Planned","release_type":"General availability"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"Oracle connector in Data Factory now allow users to connector with built-in driver. Customer now can connect to Oracle system without installing driver through on-premise gateway.","feature_name":"Connectors - Oracle connector with built-in driver","last_modified":"2026-06-16","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"4d357606-ef9d-f011-b41c-000d3a30273e","release_status":"Planned","release_type":"General availability"},{"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 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":"2026-06-16","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":"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":"2026-06-16","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"46b875fc-433d-f111-88b5-002248085b3f","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Fast copy in Dataflows Gen2","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/fast-copy-in-dataflows-gen-2","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":"2026-06-16","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"385260b0-5b07-ef11-9f89-000d3a34b75c","release_status":"Planned","release_type":"General availability"},{"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":"2026-06-16","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"37499ba5-5b21-f011-9989-000d3a5b0147","release_status":"Planned","release_type":"Public preview"},{"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":"2026-06-16","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":"Planned","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"New connectors are planned in Copy job, and Pipeline activities 1. Impala (as source)2. Jira (as source)3. Netezza (as source)3. Hive (as source)4. HubSpot (as source)5. Shopify (as source)6. Square (as source)7. Xero (as source)","feature_name":"Connectors - New connector in Copy Job and Pipeline activities","last_modified":"2026-06-16","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"34eb80a4-c49a-f011-b4cc-000d3a30273e","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://blog.fabric.microsoft.com/en-us/blog/simplifying-data-ingestion-with-copy-job-introducing-change-data-capture-cdc-support","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":"2026-06-16","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":"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 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":"2026-06-16","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":"Planned","release_type":"General availability"},{"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 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":"2026-06-16","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"1f2fe2c0-3535-f111-88b3-000d3a376c0f","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Running Apache Airflow jobs seamlessly in Microsoft Fabric","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/running-apache-airflow-jobs-seamlessly-in-microsoft-fabric","feature_description":"With Microsoft Fabric Apache Airflow Job, you can easily orchestrate your Fabric processes. 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