dbt Cloud Accelerates Speed, Productivity, and Trust with New Collaboration and Multi-Platform Analytics Features

dbt Labs, the pioneer in analytics engineering, is debuting a series of new features for dbt Cloud, the control plane for enterprise data stacks. These latest enhancements serve to empower users across the various stages of the analytics development lifecycle, adhering to One dbt, the company’s mission of delivering a single, unified dbt experience regardless of an enterprise’s infrastructure, data platform, or cloud. 

Fundamental to dbt Cloud’s update is the concept of the Analytics Development Lifecycle (ADLC), an integrated, mature analytics workflow that combats common data hurdles—such as data quality, data literacy, and data ownership—in a standardized, scalable manner. Central to this strategy is empowering a variety of personas to contribute to the analytics workflow while accelerating speed and productivity, as well as improving data trust. 

“The data industry has made real progress towards maturity over the past decade,” said Tristan Handy, founder and CEO of dbt Labs. “But real problems persist. Siloed data. Lack of trust. Too much ‘duct tape’ in our operational systems. Our announcements from this week go a long way toward fixing these gaps: One dbt experience that is cross-platform, multi-persona, trusted, and infused with AI, all facilitating a single mature workflow: the Analytics Development Lifecycle.”

The latest version of dbt Cloud centralizes metadata and makes it actionable across the ADLC workflow, supporting personas with a robust data foundation across every stage of ALDC—regardless of title, technical aptitude, or existing data platform. By democratizing data development while making it more streamlined, governed, and high quality, dbt Cloud affirms its position as the data control plane for enterprise analytics, according to the company. 

Some of the new capabilities of dbt Cloud include: 

• dbt Copilot, an AI engine in dbt Cloud that accelerates analytics workflows by automating traditionally manual tasks and enabling stakeholders to interface with data through natural language (currently in beta), improving productivity, data quality, and overall trust

• Cross-platform dbt Mesh that builds on dbt Mesh’s existing support for cross-project references, enabling users to employ cross-platform references using Iceberg table format, ultimately helping to eliminate silos and maintain governance for complex, multi-platform environments 

• Support for Apache Iceberg allows users to produce tables in Iceberg format and take advantage of Iceberg's first-class performance and portability 

• A new visual editing experience (currently in beta) imagined as a low-code, drag-and-drop environment that makes building and exploring dbt models highly accessible

• Advanced CI that enables users to compare code changes to identify unexpected behavior before new code enters production 

• Data health tiles which can be embedded into any downstream app, affording data consumers real-time context into critical trust signals such as data freshness and data quality

“dbt Core jump-started our data platform’s growth, and dbt Cloud allowed us to spread it across the globe,” said Yannick Misteli, head of engineering, global product strategy at Roche. “Today, we are able to power our platform in 70 countries and run over 15,000 models and 40,000 tests every day. We can support our core and country teams with the workflows that best suit them and promote code to production in two week cycles instead of the previous quarter or semester-long cycles.”