dbt Labs has introduced a suite of AI-powered features designed to enhance data analysts’ ability to explore, build, and validate data within governed workflows. Announced on May 28, 2025, these tools—dbt Canvas, dbt Insights, and an enhanced dbt Catalog—enable analysts to work more autonomously while maintaining organizational data standards. A new cost management dashboard also helps optimize data warehouse spending, addressing inefficiencies in analytics processes.
dbt Labs launches AI-enhanced tools: dbt Canvas, Insights, and Catalog.
dbt Canvas offers a drag-and-drop interface for easy model development.
dbt Insights enables fast querying using SQL or natural language.
Enhanced dbt Catalog improves global asset discovery for Snowflake.
Cost management dashboard optimizes data warehouse spend.
Features ensure governance while empowering analyst self-service.
dbt Canvas, now generally available, provides a visual, drag-and-drop interface that allows analysts to build and edit data models without deep SQL expertise. Integrated with dbt Copilot, it leverages natural language processing to help users create effective models. This tool reduces reliance on data engineers, fosters collaboration, and ensures governance by automatically compiling transformations into SQL within a version-controlled environment.
"dbt Canvas is unlocking a future where analysts can build confidently alongside engineers within the same trusted and governed workflows," said James Wright, Chief Strategy Officer at InterWorks. "We're excited about how this new development environment will help our customers unlock true self-service while maintaining the standards, security, and collaboration required to scale analytics responsibly."
dbt Insights, currently in preview, is an AI-powered query interface that enables analysts to ask questions and get answers quickly using SQL or natural language. Fully aware of an organization’s data models, lineage, and governance rules, it allows users to query, validate, visualize, and share findings within a single, governed workspace. This eliminates the need to switch tools or wait for central data teams, enhancing speed and productivity.
"As our data needs evolve, empowering analysts with seamless self-exploration becomes increasingly critical," said William Tsu, Senior Analytics Engineer at WHOOP. "By keeping them within the familiar dbt Catalog they already use daily, dbt's new analyst offerings enhance discoverability and enable faster, more intuitive, and governed self-service."
The expanded dbt Catalog, formerly dbt Explorer, offers a unified discovery experience for Snowflake assets, even those not managed by dbt. Now generally available, with Snowflake exploration in preview, it provides analysts with a comprehensive view of their data landscape, streamlining discovery and ensuring trust in data assets without requiring multiple tools. Integrations for additional platforms are planned.
"Lowering the technical barrier to entry for data analysts has been important to Tableau from the beginning of the company," said Dan Jewett, Senior Vice President, Product Management at Tableau. "dbt's expanded offering is a game changer for customers that are looking to reduce the sizable burden on their data engineering teams, while simultaneously enabling analysts across the business in a meaningful way."
The cost management dashboard, powered by the dbt Fusion engine and in preview for Snowflake users, provides visibility into data platform costs at the project, environment, model, and test levels. It helps organizations identify inefficiencies and realize savings by standardizing on dbt, embedding cost optimization directly into the transformation workflow.
"Data teams today face a fundamental tension – analysts need speed and independence, while organizations require strong governance and security," said Tristan Handy, founder and CEO of dbt Labs. "Our new AI-powered solutions break down these traditional barriers for data analysts across any skill level and collaborate with developers in the same platform, which will have a significant, positive impact throughout the business."
dbt Labs’ latest release empowers data analysts to contribute to the Analytics Development Lifecycle while maintaining governance and scalability. By integrating AI-driven tools and cost optimization features, dbt Labs is redefining how organizations deliver trusted, AI-ready data, driving efficiency and collaboration across data teams.
Since 2016, dbt Labs has been on a mission to help data practitioners create and disseminate organizational knowledge. dbt is the standard for AI-ready structured data. Powered by the dbt Fusion engine, it unlocks the performance, context, and trust that organizations need to scale analytics in the era of AI. Globally, more than 60,000 data teams use dbt, including those at Siemens, Roche and Condé Nast.
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