Python Notebooks now integrate with Environments, enabling users to upload and use custom libraries and packages.
","feature_name":"Environment - Support for Python Notebook","last_modified":"2026-11-10","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q1 2027","release_item_id":"f5495e72-de01-f111-8406-6045bd00f798","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"File Data Ingestion with Materialized Lake Views in Microsoft Fabric (Preview)","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/File-Data-Ingestion-with-Materialized-Lake-Views-in-Microsoft/ba-p/5367801","feature_description":"Fabric data engineers today write 80-100+ lines of boilerplate PySpark to ingest CSV and Parquet files into Delta tables - handling file discovery, schema inference, incremental refresh, schema drift, and error recovery manually. This feature introduces a simple Spark SQL DDL surface (CREATE MATERIALIZED LAKE VIEW ... FROM OneLake_Files OPTIONS (...)) that declaratively handles all of this. It includes automatic schema evolution (or strict fixed-schema mode), three refresh modes (append_only, mirror, full), built-in error handling, structured user telemetry on files and lakehouse-level DAG lineage.","feature_description_html":"Fabric data engineers today write 80-100+ lines of boilerplate PySpark to ingest CSV and Parquet files into Delta tables - handling file discovery, schema inference, incremental refresh, schema drift, and error recovery manually. This feature introduces a simple Spark SQL DDL surface (CREATE MATERIALIZED LAKE VIEW ... FROM OneLake_Files OPTIONS (...)) that declaratively handles all of this. It includes automatic schema evolution (or strict fixed-schema mode), three refresh modes (append_only, mirror, full), built-in error handling, structured user telemetry on files and lakehouse-level DAG lineage.
","feature_name":"Declarative file data ingestion experience in Fabric Materialized Lake Views","last_modified":"2026-11-10","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q3 2026","release_item_id":"14254506-0f3f-f111-88b5-6045bd00f798","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Materialized Lake Views in Microsoft Fabric (Generally Available)","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/materialized-lake-views-in-microsoft-fabric-generally-available/5172223","feature_description":"FMLV will support trigger-based execution of Materialized Lake Views -- complementing existing scheduled and ad-hoc runs -- by integrating with the Fabric event mechanism to allow customers to automatically refresh MLVs in response to events such as onelake events, Fabric Job events","feature_description_html":"FMLV will support trigger-based execution of Materialized Lake Views -- complementing existing scheduled and ad-hoc runs -- by integrating with the Fabric event mechanism to allow customers to automatically refresh MLVs in response to events such as onelake events, Fabric Job events
","feature_name":"Trigger Based Scheduling for Materialized Lake Views","last_modified":"2026-11-10","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q2 2026","release_item_id":"ec2baae9-c33e-f111-88b5-6045bd00f798","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Materialized Lake Views in Microsoft Fabric (Generally Available)","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/materialized-lake-views-in-microsoft-fabric-generally-available/5172223","feature_description":"Materialized Lake Views will soon support incremental handling of updates and deletes from source tables by defining a unique key column on the MLV itself. When rows are modified or removed in the source, all related data changes will be automatically propagated to the materialized view during the next refresh -- eliminating the need for full recomputation.","feature_description_html":"Materialized Lake Views will soon support incremental handling of updates and deletes from source tables by defining a unique key column on the MLV itself. When rows are modified or removed in the source, all related data changes will be automatically propagated to the materialized view during the next refresh -- eliminating the need for full recomputation.
","feature_name":"Materialized Lake Views Support Incremental Update & Delete Propagation via Unique Key","last_modified":"2026-11-10","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q2 2026","release_item_id":"a90a75b3-6753-f111-bec7-6045bd0066ad","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Simplifying Medallion Implementation with Materialized Lake Views in Fabric","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/simplifying-medallion-implementation-with-materialized-lake-views-in-fabric/5172766","feature_description":"FMLV will introduce column-level data quality constraints within the MLV framework -- inspired by the widely adopted Deequ library -- enabling customers to define and enforce quality rules directly on their Materialized Lake Views, addressing strong demand for built-in column-level quality controls.","feature_description_html":"FMLV will introduce column-level data quality constraints within the MLV framework -- inspired by the widely adopted Deequ library -- enabling customers to define and enforce quality rules directly on their Materialized Lake Views, addressing strong demand for built-in column-level quality controls.
","feature_name":"Support Column-Level quality checks in Materialized Lake views","last_modified":"2026-11-10","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q2 2026","release_item_id":"9c1813a3-c43e-f111-88b5-6045bd00f798","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Mastering Declarative Data Transformations with Materialized Lake Views","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/mastering-declarative-data-transformations-with-materialized-lake-views/5172617","feature_description":"Multi Lakehouse support in Lineage enables users to visualize and manage dependencies of Materialized Lake Views (MLVs) across multiple workspaces and lakehouses, providing a unified view that helps prevent data silos and improves transparency. It is designed to support scalable lineage tracking, advanced search, and focused navigation, making it easier for data teams to trace upstream and downstream dependencies.","feature_description_html":"Multi Lakehouse support in Lineage enables users to visualize and manage dependencies of Materialized Lake Views (MLVs) across multiple workspaces and lakehouses, providing a unified view that helps prevent data silos and improves transparency. It is designed to support scalable lineage tracking, advanced search, and focused navigation, making it easier for data teams to trace upstream and downstream dependencies.
","feature_name":"Fabric Materialized Lake Views - Multi Workspace/Lakehouse execution support","last_modified":"2026-11-10","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q2 2026","release_item_id":"f152e5fd-1fbf-f011-bbd3-000d3a3740cc","release_status":"Shipped","release_type":"General availability"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"This feature is to offer a model alias that provides a stable, human-readable name pointing to a specific model version. This lets them switch, promote, or roll back model versions safely without breaking downstream integrations.","feature_description_html":"This feature is to offer a model alias that provides a stable, human-readable name pointing to a specific model version. This lets them switch, promote, or roll back model versions safely without breaking downstream integrations.
","feature_name":"MLOps: model alias","last_modified":"2026-11-07","product_id":"0522b590-dcd6-ee11-9079-000d3a310f67","product_name":"Data Science","release_date":"Q2 2027","release_item_id":"761be85d-2358-f111-bec7-6045bd00f798","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Fabric Runtime 2.0 (Preview)","blog_url":"https://blog.fabric.microsoft.com/en-US/blog/fabric-runtime-2-0-preview","feature_description":"Microsoft Fabric Runtime 2.1 updates the Apache Spark foundation to Spark 4.2 and Delta Lake 4.4, with Python 3.14, Scala 2.13, and Java 21. For supported workloads, the Native Execution Engine is enabled by default to provide acceleration without extra configuration, while documented opt-out and controlled fallback paths preserve compatibility.Runtime 2.1 also introduces a leaner built-in package baseline with PySpark and SparkR. Customers can add compatible optional Python and R libraries through Library Manager or a Fabric Environment, reducing package conflicts while preserving flexibility for data engineering and data science workloads. The Public Preview is targeted for January 31, 2027 and includes migration guidance, known limitations, and preview support information.","feature_description_html":"Microsoft Fabric Runtime 2.1 updates the Apache Spark foundation to Spark 4.2 and Delta Lake 4.4, with Python 3.14, Scala 2.13, and Java 21. For supported workloads, the Native Execution Engine is enabled by default to provide acceleration without extra configuration, while documented opt-out and controlled fallback paths preserve compatibility.Runtime 2.1 also introduces a leaner built-in package baseline with PySpark and SparkR. Customers can add compatible optional Python and R libraries through Library Manager or a Fabric Environment, reducing package conflicts while preserving flexibility for data engineering and data science workloads. The Public Preview is targeted for January 31, 2027 and includes migration guidance, known limitations, and preview support information.
","feature_name":"Preview - Runtime 2.1 (Spark 4.2 and Delta 4.4)","last_modified":"2026-11-06","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q1 2027","release_item_id":"a3c892bc-d7b2-f111-aaab-70a8a59cbac4","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"New string functions and operators in Fabric Data Warehouse (Preview)","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/New-string-functions-and-operators-in-Fabric-Data-Warehouse/ba-p/5195232","feature_description":"Fuzzy string match is now generally available across Microsoft SQL offerings, including Azure SQL Database, Azure SQL Managed Instanced, SQL database in Microsoft Fabric, and SQL Server 2025. You can compare strings based on how similar or different they are, rather than relying only on exact matches. It can measure the distance between two strings, including how many changes are required to make them identical, or evaluate their similarity using different fuzzy matching algorithms. This feature makes it easier to work with imperfect or inconsistent real-world data. You can use fuzzy string match to help simplify data matching, cleansing, deduplication, fuzzy search, and other scenarios where traditional exact string comparisons may not be sufficient. [Learn more.](https://aka.ms/fuzzy-string-match)","feature_description_html":"Fuzzy string match is now generally available across Microsoft SQL offerings, including Azure SQL Database, Azure SQL Managed Instanced, SQL database in Microsoft Fabric, and SQL Server 2025. You can compare strings based on how similar or different they are, rather than relying only on exact matches. It can measure the distance between two strings, including how many changes are required to make them identical, or evaluate their similarity using different fuzzy matching algorithms. This feature makes it easier to work with imperfect or inconsistent real-world data. You can use fuzzy string match to help simplify data matching, cleansing, deduplication, fuzzy search, and other scenarios where traditional exact string comparisons may not be sufficient. Learn more.
","feature_name":"Fuzzy string match is available across Microsoft SQL offerings","last_modified":"2026-11-06","product_id":"347da228-ea54-ef11-a317-0022480a694f","product_name":"SQL database","release_date":"Q4 2026","release_item_id":"afcd2c3d-5bb2-f111-aaab-70a8a5afd4dd","release_status":"Shipped","release_type":"General availability"},{"active":true,"blog_title":"Extending Point-in-Time Retention in Fabric SQL DB: From 7 to 35 Days","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/extending-point-in-time-retention-in-fabric-sql-db-from-7-to-35-days/5172554","feature_description":"Today users can restore a database into any point-in-time from live databases in Microsoft Fabric. Point-in-time restore (PITR) feature should have enhanced capability to help their users recover from accidental database deletion and ensure business continuity. Users should be able to restore a dropped database from a hard-deleted artifact and from workspace soft-deleted workspace as well. In each use-case, the user should be able to easily restore a database and continue using it.","feature_description_html":"Today users can restore a database into any point-in-time from live databases in Microsoft Fabric. Point-in-time restore (PITR) feature should have enhanced capability to help their users recover from accidental database deletion and ensure business continuity. Users should be able to restore a dropped database from a hard-deleted artifact and from workspace soft-deleted workspace as well. In each use-case, the user should be able to easily restore a database and continue using it.
","feature_name":"Point-in-time restore from dropped databases","last_modified":"2026-11-06","product_id":"347da228-ea54-ef11-a317-0022480a694f","product_name":"SQL database","release_date":"Q4 2026","release_item_id":"0ba89ee5-81b3-f111-aaab-70a8a59cbac4","release_status":"Shipped","release_type":"General availability"},{"active":true,"blog_title":"Resolving Write Conflicts in Microsoft Fabric Data Warehouse","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/resolving-write-conflicts-in-microsoft-fabric-data-warehouse/5172544","feature_description":"Improve write concurrency in Microsoft Fabric Warehouse with file-level conflict detection. Concurrent updates and deletes that affect different data files within the same table can commit without unnecessary write conflicts, reducing transaction failures and retries while maintaining data consistency.","feature_description_html":"Improve write concurrency in Microsoft Fabric Warehouse with file-level conflict detection. Concurrent updates and deletes that affect different data files within the same table can commit without unnecessary write conflicts, reducing transaction failures and retries while maintaining data consistency.
","feature_name":"File-Level write-write conflict detection (Generally Available)","last_modified":"2026-11-05","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"Q4 2026","release_item_id":"b7e155c6-94bf-f011-bbd3-000d3a5b0efa","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":"dbt job introduces native authoring and operationalization of dbt workflows within Microsoft Fabric, eliminating the need for external orchestration tools. This feature enables customers to build, schedule, and manage dbt SQL transformations in Fabric Data Warehouse and external data wareshouses, streamlining data transformation processes and improving productivity.","feature_description_html":"dbt job introduces native authoring and operationalization of dbt workflows within Microsoft Fabric, eliminating the need for external orchestration tools. This feature enables customers to build, schedule, and manage dbt SQL transformations in Fabric Data Warehouse and external data wareshouses, streamlining data transformation processes and improving productivity.
","feature_name":"dbt job - New dbt job support in Fabric","last_modified":"2026-11-05","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"ac18ff0a-9f06-ef11-9f89-000d3a33ff0b","release_status":"Shipped","release_type":"General availability"},{"active":true,"blog_title":"Fabric Runtime Release Channels","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Fabric-Runtime-Release-Channels/ba-p/5240330","feature_description":"This feature enables multiple runtime channels for customers. The default channel will remain the current standard runtime, while an EarlyAccess channel will provide the latest updates - such as library upgrades and security vulnerability fix..Using Spark configuration, customers can test and validate these changes early, before they become part of the default runtime channel.","feature_description_html":"This feature enables multiple runtime channels for customers. The default channel will remain the current standard runtime, while an EarlyAccess channel will provide the latest updates - such as library upgrades and security vulnerability fix..Using Spark configuration, customers can test and validate these changes early, before they become part of the default runtime channel.
","feature_name":"Synapse Release Channel - Public Preview","last_modified":"2026-11-05","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q3 2026","release_item_id":"3665d185-5b43-f111-88b5-6045bd0a886d","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Fabric Runtime 2.0 (Preview)","blog_url":"https://blog.fabric.microsoft.com/en-US/blog/fabric-runtime-2-0-preview","feature_description":"This a new Fabric Runtime 2.0 release based on Spark 4.x and Delta Lake 4.x. Most importantly, it will have Scala 2.13 and will be based on Mariner 3.0 OS.","feature_description_html":"This a new Fabric Runtime 2.0 release based on Spark 4.x and Delta Lake 4.x. Most importantly, it will have Scala 2.13 and will be based on Mariner 3.0 OS.
","feature_name":"Fabric Runtime 2.0 - General Availability","last_modified":"2026-11-05","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q3 2026","release_item_id":"ac3295f6-95ba-f011-bbd3-00224808fcf0","release_status":"Shipped","release_type":"General availability"},{"active":true,"blog_title":"Fabric Databases\u2014\u00a0a unified, SaaS-native experience for modern data workloads (Generally Available)","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/fabric-databases%E2%80%94%C2%A0a-unified-saas-native-experience-for-modern-data-workloads-gen/5172485","feature_description":"Fabric HorizonDB is a new serverless, Fabric-native PostgreSQL database built for modern data and AI applications. Fabric HorizonDB brings operational data directly into Fabric, giving developers a PostgreSQL foundation alongside Fabric's analytics, data, and AI capabilities.With new capabilities that connect transactional and analytical data across Fabric, developers can bring operational data into analytics and AI experiences while connecting insights back to their applications. And with Fabric HorizonDB available as a database option for Fabric apps, teams have a connected foundation for building intelligent applications on their data.","feature_description_html":"Fabric HorizonDB is a new serverless, Fabric-native PostgreSQL database built for modern data and AI applications. Fabric HorizonDB brings operational data directly into Fabric, giving developers a PostgreSQL foundation alongside Fabric's analytics, data, and AI capabilities.With new capabilities that connect transactional and analytical data across Fabric, developers can bring operational data into analytics and AI experiences while connecting insights back to their applications. And with Fabric HorizonDB available as a database option for Fabric apps, teams have a connected foundation for building intelligent applications on their data.
","feature_name":"Microsoft Fabric HorizonDB","last_modified":"2026-11-04","product_id":"0d409704-5dd2-f111-a059-0022480a4cb7","product_name":"Fabric HorizonDB","release_date":"Q4 2026","release_item_id":"29a59504-db74-f111-ab0f-002248085b3f","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Copy job for SAP with ABAP Add-On in Microsoft Fabric (Preview)","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Copy-job-for-SAP-with-ABAP-Add-On-in-Microsoft-Fabric-Preview/ba-p/5196120","feature_description":"Copy job for SAP with ABAP add-on enables organizations to bring SAP data into Microsoft Fabric using a Microsoft Data Integration ABAP Add-On installed on the SAP server. It extends Fabric's built-in SAP connectors and supports copying tables, views, and CDS views from supported SAP systems with both full and incremental copy modes.","feature_description_html":"Copy job for SAP with ABAP add-on enables organizations to bring SAP data into Microsoft Fabric using a Microsoft Data Integration ABAP Add-On installed on the SAP server. It extends Fabric's built-in SAP connectors and supports copying tables, views, and CDS views from supported SAP systems with both full and incremental copy modes.
","feature_name":"Copy job - SAP with ABAP add-on","last_modified":"2026-11-04","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"07513d58-4161-f111-a826-6045bd00fc61","release_status":"Shipped","release_type":"General availability"},{"active":true,"blog_title":"Introducing Apache Airflow job in Microsoft Fabric","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/introducing-apache-airflow-job-in-microsoft-fabric/5173206","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_description_html":"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":"2026-11-03","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q1 2027","release_item_id":"ef14c40e-0a6f-f011-bec2-00224804b6c3","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_description_html":"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":"2026-11-03","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q1 2027","release_item_id":"89223235-516f-f111-ab0d-6045bd006301","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Announcing preview of Workspace Monitoring","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/announcing-preview-of-workspace-monitoring/5173019","feature_description":"This feature delivers a comprehensive Spark Workspace Monitoring experience, enabling administrators and data engineers to gain full visibility into workspace-level Spark activity. Users can view and analyze live and historical job executions, filter and drill into specific runs, and compare performance across Fabric items.With direct access to Spark diagnostic logs and metrics via Kusto Query Language (KQL), users can create custom queries, perform advanced aggregations, and build their own dashboards, unlocking powerful, flexible insights for monitoring, troubleshooting, and optimization.","feature_description_html":"This feature delivers a comprehensive Spark Workspace Monitoring experience, enabling administrators and data engineers to gain full visibility into workspace-level Spark activity. Users can view and analyze live and historical job executions, filter and drill into specific runs, and compare performance across Fabric items.With direct access to Spark diagnostic logs and metrics via Kusto Query Language (KQL), users can create custom queries, perform advanced aggregations, and build their own dashboards, unlocking powerful, flexible insights for monitoring, troubleshooting, and optimization.
","feature_name":"Spark Workspace Monitoring v1","last_modified":"2026-11-03","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q4 2026","release_item_id":"f0f360ab-3350-f111-bec7-6045bd00fc61","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Build more connected workflows with Fabric Actions and Business Actions in Fabric Pipelines (Preview)","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Build-more-connected-workflows-with-Fabric-Actions-and-Business/ba-p/5377911","feature_description":"The Fabric Actions activity enables users to invoke supported Microsoft Fabric REST API operations directly from Fabric Data Factory pipelines through a guided, low-code experience.The Business Actions activity enables users to invoke supported enterprise application operations through Azure Logic Apps connectors directly from Fabric Data Factory pipelines.","feature_description_html":"The Fabric Actions activity enables users to invoke supported Microsoft Fabric REST API operations directly from Fabric Data Factory pipelines through a guided, low-code experience.
\nThe Business Actions activity enables users to invoke supported enterprise application operations through Azure Logic Apps connectors directly from Fabric Data Factory pipelines.
","feature_name":"Pipelines - Fabric Actions and Business Actions activities","last_modified":"2026-11-03","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"}],"links":{"first":"/api/releases?modified_within_days=7&page_size=25&page=1","last":"/api/releases?modified_within_days=7&page_size=25&page=1","next":null,"prev":null,"self":"/api/releases?modified_within_days=7&page_size=25&page=1"},"pagination":{"has_next":false,"has_prev":false,"next_page":null,"page":1,"page_size":25,"prev_page":null,"total_items":20,"total_pages":1}}