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"}],"links":{"first":"/api/releases?page_size=10&page=1","last":"/api/releases?page_size=10&page=113","next":"/api/releases?page_size=10&page=2","prev":null,"self":"/api/releases?page_size=10&page=1"},"pagination":{"has_next":true,"has_prev":false,"next_page":2,"page":1,"page_size":10,"prev_page":null,"total_items":1124,"total_pages":113}}