{"data":[{"active":true,"blog_title":"Mastering Declarative Data Transformations with Materialized Lake Views","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/mastering-declarative-data-transformations-with-materialized-lake-views","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_name":"Declarative file data ingestion experience in Fabric Materialized Lake Views","last_modified":"2026-07-30","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":"Planned","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"This feature delivers a comprehensive Notebook Run Lineage experience for Spark applications, enabling data engineers to monitor and analyze complex notebook workflows with ease. It provides an at-a-glance view of all notebook run statuses, helps quickly identify root-cause failures across nested executions, and highlights performance bottlenecks. Users can filter and explore large execution graphs, access detailed error and run-level information, and view aggregated summaries of execution health and resource usage--all in one place.","feature_name":"Track and Manage Notebook Run Dependencies & Linage View","last_modified":"2026-07-27","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q3 2026","release_item_id":"71469140-3550-f111-bec7-6045bd00fc61","release_status":"Planned","release_type":"Public preview"},{"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_name":"Synapse Release Channel - Public Preview","last_modified":"2026-07-27","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":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Announcing Eventhouse Query Acceleration for OneLake Shortcuts (Preview)","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/announcing-query-acceleration-for-onelake-shortcuts-public-preview","feature_description":"On the data exploration front, the new Lakehouse Query Explorer introduces a streamlined, in-context query experience directly within the Lakehouse. Users can write and run Spark SQL queries right from the Lakehouse explorer to quickly discover and analyze data, with intelligent suggestions and real-time error prevention all without needing to navigate to a notebook. Results can be saved as Spark Views, visualized as charts, filtered, sorted, and downloaded, with rich contextual information throughout. When deeper analysis is required, code can be seamlessly promoted into a notebook for more complex processing, bridging the gap between ad-hoc exploration and production-grade data engineering.","feature_name":"Lakehouse Query Window","last_modified":"2026-07-27","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q2 2026","release_item_id":"e5bc7caf-294e-f111-bec7-000d3a376137","release_status":"Shipped","release_type":"General availability"},{"active":true,"blog_title":"Fabric Runtime 1.3 is Generally Available! Upgrade your data engineering and science workloads to harness the latest innovations and performance enhancements","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/fabric-runtime-1-3-is-generally-available-upgrade-your-data-engineering-and-science-workloads-to-harness-the-latest-innovations-and-performance-enhancements","feature_description":"We are upgrading the underlying operating system of Fabric Runtime 1.3 from Mariner 2.0 to Mariner 3.0, delivering improved security, performance, and long-term support for Apache Spark workloads. This upgrade brings the latest OS-level patches, enhanced container compatibility, and a modernized base image that aligns with the latest Azure infrastructure standards.To ensure a smooth transition, the upgrade leverages the Runtime Release Channel feature, which allows customers to preview and validate the Mariner 3.0-based runtime before it becomes the default. Data engineers can switch to the release candidate channel in their environment settings, run their existing workloads, and confirm compatibility -- all before the update is applied across their organization.Key benefits include:Enhanced security posture with the latest OS-level patches and hardeningImproved performance from the modernized Mariner 3.0 baseCustomer-controlled validation through the release channel, reducing risk of unexpected issuesContinued compatibility with all existing Runtime 1.3 workloads and librariesThe Mariner 3.0 upgrade is available through Fabric environment settings with no additional configuration required.Business Value: Strengthens the security and performance foundation of Fabric Runtime 1.3 by upgrading to Mariner 3.0, while giving customers the ability to validate the change before it rolls out to production.","feature_name":"Fabric Runtime 1.3 Mariner 3.0 Upgrade","last_modified":"2026-07-27","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q2 2026","release_item_id":"b8bed2a5-5943-f111-88b5-6045bd0a886d","release_status":"Shipped","release_type":"Public preview"},{"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 settings in the environment, customers can test and validate these changes early, before they become part of the default runtime channel.","feature_name":"Fabric Release Channel - Public Preview","last_modified":"2026-07-27","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q2 2026","release_item_id":"399f07f8-96ba-f011-bbd3-00224808fcf0","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"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":"2026-07-15","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q3 2026","release_item_id":"f5495e72-de01-f111-8406-6045bd00f798","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Fabric Notebooks: Resources Folder Support in Git","blog_url":"https://blog.fabric.microsoft.com/en-US/blog/fabric-notebooks-resources-folder-support-in-git","feature_description":"Modules and files stored in the Notebook Resource folder can now be committed to Git and published through Deployment Pipelines.","feature_name":"CI/CD: notebook resources in git and deployment pipeline and support for API","last_modified":"2026-07-15","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q3 2026","release_item_id":"f4f6862a-e001-f111-8406-6045bd00f798","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Enhanced Monitoring for Spark High Concurrency Workloads in Microsoft Fabric","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/enhanced-monitoring-for-spark-high-concurrency-workloads-in-microsoft-fabric","feature_description":"This feature is designed to visualize real-time metrics for Spark applications through a dashboard that allows users to monitor CPU and memory utilization at the application level for both executors and drivers, across running and completed applications. It aligns with the Fabric SaaS offering and extends Spark vCore allocation and utilization analysis. Additional details about user scenarios are available in the discussion.","feature_name":"[Monitoring] Integrate and Expose Spark Application CPU/Memory Usage","last_modified":"2026-07-01","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q4 2026","release_item_id":"fe489766-3350-f111-bec7-6045bd00fc61","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Gain Deeper Insights into Spark Jobs with JobInsight in Microsoft Fabric","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/gain-deeper-insights-into-spark-jobs-with-jobinsight-in-microsoft-fabric","feature_description":"The general availability of the Job Insights Library provides comprehensive visibility into Spark execution by surfacing logs and metrics across queries, jobs, stages, and tasks. Users can not only analyze execution behavior directly within the experience but also download and store driver and executor logs to Lakehouse or other destinations, enabling persistent storage, deeper analysis, and integration with downstream monitoring workflows.","feature_name":"[GA] Job Insights Library","last_modified":"2026-07-01","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q4 2026","release_item_id":"add43c89-3350-f111-bec7-6045bd00fc61","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Announcing the Fabric Apache Spark Diagnostic Emitter: Collect Logs and Metrics","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/announcing-the-fabric-apache-spark-diagnostic-emitter-collect-logs-and-metrics","feature_description":"This feature updates the Spark diagnostic emitter to support the upcoming Spark 4.1 release while maintaining full compatibility with existing capabilities, including Log Analytics (legacy and new APIs), Event Hub, and Blob Storage.","feature_name":"Spark Diagnostic Emitter for Spark 4.1","last_modified":"2026-07-01","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q3 2026","release_item_id":"97bef576-3550-f111-bec7-6045bd00fc61","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"Visibility into Job Concurrency & QueueingWorkspace users and admins can see all active jobs and their states (running, queued, throttled)Diagnose job delays by identifying concurrency limits or queueing bottlenecksCapacity admins can now monitor job activity across all workspaces based on CU load.Understand overall load and capacity pressure","feature_name":"Display the job concurrency and queueing limits to users as part of workspace settings","last_modified":"2026-07-01","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q3 2026","release_item_id":"1a98d2aa-7bba-f011-bbd2-0022480a2ecf","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"How Spark Supports OneLake Security with Row and Column Level Policies","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/how-spark-supports-onelake-security-with-row-and-column-level-security-policies","feature_description":"OneLake Security supports Dynamic Row-Level Security (RLS), so each user sees only the rows that match their identity. This work brings Spark on Fabric into parity with that capability: Spark queries against tables protected by a Dynamic RLS role correctly return only the rows the calling user is allowed to see.With this release, Security Admins can define a single set of Dynamic RLS roles in OneLake Security and have those policies enforced uniformly across Spark, T-SQL, and Power BI. Existing Dynamic RLS roles defined in OneLake Security start working for Spark notebooks and jobs without any policy or query changes. Customers no longer have to choose between Dynamic RLS and Spark access for the same dataset.","feature_name":"Dynamic RLS support in Spark","last_modified":"2026-06-16","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q3 2026","release_item_id":"ff6d38a5-4a4d-f111-bec6-6045bd00f798","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Announcing preview of Workspace Monitoring","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/announcing-public-preview-of-workspace-monitoring","feature_description":"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":"2026-06-16","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q3 2026","release_item_id":"f0f360ab-3350-f111-bec7-6045bd00fc61","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"How Spark Supports OneLake Security with Row and Column Level Policies","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/how-spark-supports-onelake-security-with-row-and-column-level-security-policies","feature_description":"OneLake is adding support for multi-table Row-Level Security (RLS) to OneLake security. This allows users to define security for one table based on values of columns in another table. This is commonly used for filtering scenarios such as restricting sales data based on assigned stores or regions.This work item adds the necessary functionality to the Spark workload so that multi-table RLS policies can be defined and enforced. For workloads using the RLS bitmaps, the implementation requires testing and validation work to ensure correct enforcement during scan execution.","feature_name":"OneSecurity multi-table RLS support in Spark","last_modified":"2026-06-16","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q3 2026","release_item_id":"e54fa8e7-494d-f111-bec6-000d3a376c0f","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Fabric Runtime 1.3 is Generally Available! Upgrade your data engineering and science workloads to harness the latest innovations and performance enhancements","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/fabric-runtime-1-3-is-generally-available-upgrade-your-data-engineering-and-science-workloads-to-harness-the-latest-innovations-and-performance-enhancements","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_name":"Fabric Runtime 2.0 - General Availability","last_modified":"2026-06-16","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":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Simplifying Medallion Implementation with Materialized Lake Views in Fabric","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/announcing-materialized-lake-views-at-build-2025","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_name":"Trigger Based Scheduling for Materialized Lake Views","last_modified":"2026-06-16","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":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Native Execution Engine available at no additional cost!","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/native-execution-engine-available-at-no-additional-cost","feature_description":"Enhance the Native Execution Engine in Microsoft Fabric Spark to support CSV ingestion natively, minimizing fallbacks to the Spark JVM and improving performance for data ingestion workflows.Unlock native engine speedups for foundational ingestion scenarios.Reduce cost and latency during ETL, especially for write-heavy delta loads for customers given majority of users  have CSV based file dependencies","feature_name":"CSV Support for Native Execution Engine","last_modified":"2026-06-16","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q2 2026","release_item_id":"bfd0bc17-7dba-f011-bbd2-0022480a2ecf","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":null,"blog_url":null,"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_name":"Materialized Lake Views Support Incremental Update & Delete Propagation via Unique Key","last_modified":"2026-06-16","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":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Simplifying Medallion Implementation with Materialized Lake Views in Fabric","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/announcing-materialized-lake-views-at-build-2025","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_name":"Support Column-Level quality checks in Materialized Lake views","last_modified":"2026-06-16","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":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Custom Live Pools for Fabric Data Engineering (Preview)","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Custom-Live-Pools-for-Fabric-Data-Engineering-Preview/ba-p/5187356","feature_description":"Customers can create custom compute pools for Spark with libraries and other items specific to their scenario and keep them warm like they can today with starter pools.","feature_name":"Custom Live Pools","last_modified":"2026-06-16","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q2 2026","release_item_id":"11fd2c23-e28c-ef11-ac21-00224804e9b4","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Simplifying Medallion Implementation with Materialized Lake Views in Fabric","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/announcing-materialized-lake-views-at-build-2025","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_name":"Fabric Materialized Lake Views - Multi Workspace/Lakehouse execution support","last_modified":"2026-06-16","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":"Planned","release_type":"General availability"},{"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_name":"Fabric Runtime 2.0 - Public Preview","last_modified":"2026-06-16","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q2 2026","release_item_id":"74116db5-95ba-f011-bbd3-00224808fcf0","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Announcing the Fabric Apache Spark Diagnostic Emitter: Collect Logs and Metrics","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/announcing-the-fabric-apache-spark-diagnostic-emitter-collect-logs-and-metrics","feature_description":"This feature is intended to announce the General Availability of the Fabric Spark diagnostic emitter. The Spark emitter enables customers to send Spark logs and metrics to their preferred destinations, including Azure Log Analytics, Azure Event Hub, and Azure Blob Storage.","feature_name":"Fabric Spark Diagnostic Log Emitter: General Availability","last_modified":"2026-06-16","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q1 2026","release_item_id":"2ff1693f-85ba-f011-bbd3-6045bd00f9db","release_status":"Shipped","release_type":"General availability"},{"active":true,"blog_title":"Enhancing AI productivity in Fabric notebooks with Copilot updates","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/enhancing-ai-productivity-in-fabric-notebooks-with-copilot-updates","feature_description":"The new Notebook Copilot experience is Fabric-context aware, fast, and agentic--able to reason over your workspace and write code directly from natural-language prompts. It provides contextual assistance tailored to Data Engineers and Data Scientists, helping them move from intent to executable notebooks with less friction.","feature_name":"Agentic Copilot for Notebooks","last_modified":"2026-06-16","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q1 2026","release_item_id":"5fa7544e-c4c3-f011-bbd3-00224808fcf0","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Fabric Data Agents + Microsoft Copilot Studio: A New Era of Multi-Agent Orchestration (Preview)","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/fabric-data-agents-microsoft-copilot-studio-a-new-era-of-multi-agent-orchestration","feature_description":"The new Fabric Notebook agent mode, available in the GitHub Copilot panel in VS Code, enables users to leverage GitHub Chat with Fabric-aware context across Notebooks, Lakehouses, notebookutils, and more.","feature_name":"VSCode FabricNotebook Agent mode","last_modified":"2026-06-16","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q1 2026","release_item_id":"743b7ba3-dc01-f111-8406-6045bd00f798","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Simplifying Medallion Implementation with Materialized Lake Views in Fabric","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/announcing-materialized-lake-views-at-build-2025","feature_description":"Multiple schedule in FMLV allows users to independently schedule refreshes for individual Materialized Lake Views or chains within a Lakehouse, rather than applying a single schedule to the entire LakeHouse. This enables targeted refreshes, optimizes compute usage, and aligns data freshness with specific business SLAs for different reporting","feature_name":"Fabric Materialized Lake Views - Multiple Schedule Support","last_modified":"2026-06-16","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q1 2026","release_item_id":"25c46393-1fbf-f011-bbd3-000d3a3740cc","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"This new lightweight capability, available in the environment, provides an agile option to install libraries and packages using a quick mode, ideal for workloads that require frequent iteration. The quick mode can be switched to full mode later if needed.","feature_name":"Environment - Lightweight Library management","last_modified":"2026-06-16","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q1 2026","release_item_id":"9c3c2e19-c5c3-f011-bbd3-00224808fcf0","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Resource Profiles in Microsoft Fabric Data Engineering (Preview)","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Resource-Profiles-in-Microsoft-Fabric-Data-Engineering-Preview/ba-p/5182862","feature_description":"Performance by Default Experience based simple hints from users based on their workload requirementsPre-configured compute and environment settings tailored to specific data engineering workloads based on their requirements and price perf goals from workspace settings","feature_name":"Resource Profiles for Fabric Data Engineering","last_modified":"2026-06-16","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q1 2026","release_item_id":"be21e5f9-7dba-f011-bbd2-0022480a2ecf","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Microsoft JDBC Driver for Microsoft Fabric Data Engineering (Preview)","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/microsoft-jdbc-driver-for-microsoft-fabric-data-engineering-preview","feature_description":"This feature provides the Spark JDBC driver as a downloadable component for use within client applications. 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