Power BI

Accelerate app development: Cosmos DB (NoSQL) in Fabric now in preview

This removes the barriers between users and insights, enabling everyone—from business analysts to data scientists—

engage in natural language conversations with their data across multiple reports and semantic models

This chat with your data experience will allow users to ask broader questions and intelligently retrieve the most relevant data

Coming soon in public preview, Fabric data agents can be added to any custom agent built in Microsoft Copilot Studio

Once connected, the custom agent uses the Fabric data agent to retrieve insights from OneLake, respecting data access permissions

Developers can also define actions (e.g., send an email or trigger workflows) to automate processes

For teams tasked with building new AI and analytics solutions, finding and accessing the necessary data across a sea of disconnected data services

Shortcut transformations

These updates reinforce our commitment to Fabric’s four core pillars: A complete, AI-powered data platform. An open, AI-ready data lake. Empowering AI-enabled business users. A mission-critical foundation.

The general availability of the Native Execution Engine enables Spark queries to run natively on your lakehouse—up to 6x faster with no code changes or vendor lock-in. This release brings built-in optimizations and resource profiles for faster, more cost-effective data engineering at scale.

real-time endpoints for ML models

The preview of Warehouse Snapshots enables users to access a consistent view of data from a specific point in time, even during ETL processes

Copilot for SQL Analytics Endpoint in the context of your business Picture this: you need to tie customer orders from your mirrored CRM to fulfillment data in the Warehouse – something that normally requires digging through schemas, writing complex joins, and double-checking table relationships. With Copilot, you skip the heavy lifting. A simple prompt – ‘Get customer orders from CRM and join with fulfillment data from the warehouse‘ – returns a ready-to-run query that pulls exactly what you need, no manual schema deep dives required. Or maybe you’re looking to blend product metadata in a Lakehouse with sales numbers stored in a Warehouse. Instead of bouncing between storage formats and trying to reconcile differences yourself, you ask Copilot: ‘Show me top-selling products by category using product metadata from the Lakehouse and sales from the warehouse.‘ Copilot does the heavy lifting, stitching sources together behind the scenes so you can focus on the insight, not the integration. And when you’re under pressure to deliver quick insights – like pulling revenue by region for a quarterly review – Copilot has you covered there, too. Even if you’ve never touched the Finance Lakehouse, you can ask: ‘Show me total revenue by region for the last quarter using the Finance Lakehouse.‘ Copilot identifies the right tables, applies the correct filters, and generates the SQL you need without the typical back-and-forth. Copilot transforms how you work with data: no more searching, stitching, or second-guessing. Complex environments become accessible. Insights flow faster. Data silos disappear. With Copilot in SQL Analytics Endpoint, you don’t just save time, you unlock the full power of Fabric and move from question to impact without missing a beat.

LLM tools: Likewise, you can use LLM tools to facilitate various aspects of report development, including the creation of custom visuals in DAX (via SVG visuals), R, Python, and Deneb.

Figma and Excalidraw: general-purpose design tools that you can use to facilitate the design for anything. For models, you might use Figma (FigJam, likely) or Excalidraw to create the conceptual or logical model for your semantic model during the design There are many other tools for doing this, including Visio or even the diagram view in Tabular Editor 3.

Tabular Editor: an end-to-end development tool that you can use either together with Power BI Desktop or as an alternative. Tabular Editor is an integrated development environment specialized for working with semantic models. It is designed from the ground up, to empower semantic model developers with everything they need to build good semantic models while being efficient.

Large language models (LLMs) and other generative AI tools

Power BI Desktop tools: New features and tools in Power BI Desktop also support a variety of use cases.

Power Designer

It is important to emphasize that you do not need to use any of these tools. Rather, you typically turn to them when you have specific problems that you want to solve, or scenarios that you aim to address