Microsoft Fabric offers native Git integration and deployment pipelines to facilitate version control, collaboration, and automated releases for workspace items like user data functions. This guide explains how to set up and manage Git integration for user data functions within a Fabric workspace.
• Workspace preparation and Git linking: Users start by selecting or creating a Fabric workspace containing user data functions, then enable Git integration via workspace settings by connecting to a Git provider and repository branch, optionally specifying a folder for organization.
• Branching strategy configuration: Teams are advised to adopt branching strategies such as main/develop, feature, and release branches, along with pull request and code review policies to maintain code quality and collaboration.
• Managing user data functions in Git: Each data function is stored in a function_app.py file; users clone the repository locally, edit or add functions, and update the definition.json file to reflect new functions and required libraries like numpy.
• Committing, syncing, and publishing changes: After committing changes in VS Code, users sync with the Fabric portal, update the function via source control, and publish to deploy the new or updated functions, making them available for invocation.
Microsoft Fabric has introduced new features for its User Data Functions (UDFs), enhancing Python-based data processing capabilities within the platform. These updates include support for asynchronous functions and the use of pandas DataFrame and Series types for input and output, enabling more efficient handling of large-scale data.
• Async function support: Developers can now write async functions in Fabric UDFs to improve responsiveness and efficiency, especially for managing high volumes of I/O-bound operations, such as reading files asynchronously from a Lakehouse.
• Pandas DataFrame and Series integration: UDFs can accept and return pandas DataFrames and Series, allowing batch processing of rows with improved speed and performance in data analysis tasks. An example function calculates total revenue by driver using pandas groupby operations.
• Usage in notebooks: These functions can be invoked directly from notebooks using pandas objects, facilitating efficient aggregation and analysis of large datasets interactively within Microsoft Fabric.
• Getting started and benefits: Users can enable these features by updating the fabric-user-data-functions library to version 1.0.0. The enhancements reduce I/O operations, enable concurrent task handling, and improve performance on datasets with millions of rows.
Effortlessly read Delta Lake tables using Apache Iceberg readers Microsoft Fabric is a unified, SaaS data and analytics platform designed for the era of AI. All workloads in Microsoft Fabric use Delta Lake as the standard, open-source table format. With Microsoft OneLake, Fabric’s unified SaaS data lake, customers can unify their data estate across multiple …
Continue reading “New in OneLake: Access your Delta Lake tables as Iceberg automatically (Preview)”
Have you ever wondered how to give AI assistants access to your organization’s data in a clean, structured way? The Model Context Protocol (MCP) is an open standard that creates a bridge between large language models and external tools, APIs, and data sources. Think of it as a universal translator that lets AI agents understand …
Continue reading “Connecting AI Agents to Microsoft Fabric with GraphQL and the Model Context Protocol (MCP)”
The Microsoft Fabric Extension for VS Code introduces two new features that enhance the management of Fabric items directly within the editor. Users can now perform CRUD operations on Fabric items and switch between multiple tenants easily. These updates aim to improve workflow efficiency and are based on customer feedback, inviting further suggestions for enhancement.
