Fabric

When you provide the AI with sample query/question pairs, it references these examples when it answers future questions. Matching new queries to the most relevant examples helps the AI incorporate business-specific logic, and respond effectively to commonly asked questions. This functionality enables fine-tuning for individual data sources, and ensures generation of more accurate SQL or KQL queries. Power BI semantic model data don't support adding sample query/question pairs at this time. However, for supported data sources such as lakehouse, warehouse, and KQL databases, providing more examples can significantly improve the AI's ability to generate precise queries when its default performance needs adjustment.

By tailoring these instructions and defining terms, you enhance the AI's ability to deliver precise and relevant insights, in full alignment with your data strategy and business requirements.

Use Fabric User Data Functions with Pandas DataFrames and Series in Notebooks

Use Fabric User Data Functions with Pandas DataFrames and Series in Notebooks A major upgrade to Notebook integration with Fabric User Data Functions (UDFs) is now available: Pandas DataFrames and Series can now be used as input and output types—thanks to native integration with Apache Arrow! This update brings higher performance, improved efficiency, and greater scalability to your Fabric Notebooks—enabling seamless function reuse for large-scale data processing across Python, PySpark, Scala, and R. With this release, Pandas DataFrames and Series are now supported as first-class input and output types for UDFs, enabled by deep integration with Apache Arrow, a highly efficient columnar memory format optimized for analytics workloads.

Microsoft Fabric APIs Specification I’m excited to share that we’ve successfully published the Microsoft Fabric APIs Specification in the microsoft/fabric-rest-api-specs GitHub repository!

The Functions portal includes a Generate invocation code feature that allows for automatic generation of an Open API specification for Fabric User Data Functions.

OpenAPI spec generation in Fabric User Data Functions (Preview) The Functions portal includes a Generate invocation code feature that allows for automatic generation of an Open API specification for Fabric User Data Functions.

Expanded Data Agent Support for Large Data Sources (Preview) Data Agent is officially lifting restrictions on adding Data Sources with larger schema sizes. Users can now add Kusto, Semantic Model, Lakehouse, and Warehouse Data sources that contain over 100 Columns + Measures and more than 1000 Tables to the Data Agent. This change allows users to bring larger-scale databases and semantic models into Fabric’s Data Agent, unlocking deeper insights and enhanced capabilities.

Bulk delete query: This feature enables users to delete multiple saved queries at once, eliminating the need to remove them individually. It was introduced in response to user feedback highlighting the difficulty of managing large lists of saved queries without a multi-select delete option. In the query editor’s ‘Queries’ folders, users can now hold Shift, select multiple queries, and right-click to delete them all in a single action. This streamlines the cleanup process, making it easier to maintain an organized and clutter-free workspace with minimal effort.

Open your database in SQL Server Management Studio (SSMS): This feature integrates the Fabric SQL web-based editor with SSMS, allowing for a smooth transition to the desktop environment. With a single click from the query editor, SSMS launches and automatically fills in the connection details for your Fabric SQL database — no manual copy-paste or setup required. This streamlines the workflow for users who prefer or need the advanced capabilities and richer UI of SSMS, making it faster and easier to switch between tools while working with Fabric SQL.

Use Python Notebooks to Read/Write to Fabric SQL Databases (Preview) You can now read from and write to SQL databases in Microsoft Fabric using Python Notebooks, thanks to the new integration with the T-SQL magic command. This highly requested feature enables users to run powerful T-SQL queries directly within notebooks—combining scripting, visualizations, and explanatory text in one collaborative workspace. It supports rich, interactive charts, automated workflows, scheduled jobs, and secure sharing, making it easier than ever to analyze and operationalize SQL data seamlessly across the Fabric platform.

Microsoft Fabric APIs Specification

Notebook snapshot for running Notebooks

OpenAPI spec generation in Fabric User Data Functions (Preview)

Expanded Data Agent Support for Large Data Sources (Preview)

Use Python Notebooks to Read/Write to Fabric SQL Databases (Preview)

Optimizing Query Management: New Controls in the Editor

Create CI/CD-Enabled Dataflow Gen2 from Existing Dataflow Gen2 (Generally Available) Integrated Run History and Validation Feedback in Dataflow Gen2 Editor Improvements to SharePoint as a destination in Dataflow Gen2

New Category Filters Added to Template Gallery