New dbt Cloud Enhancements Empower Organizations with Trustworthy Data At Scale

Leader in the cloud data ecosystem announces AI and low code development capabilities, among other features, to improve AI and analytics workflows, and enable more people to build and leverage data

PHILADELPHIA, May 14, 2024 /PRNewswire/ -- dbt Labs, the pioneer in analytics engineering, today announced dbt Cloud enhancements designed to help businesses turn data into a competitive advantage. As companies' data volumes explode and the need for trustworthy, high-quality data increases, dbt Cloud is meeting the market need for streamlined data transformation across pipelines, workflows, and teams.

"Accurate and timely data is crucial, which is why we've delivered a standardized way to quickly build reliable, holistic, and high-quality data pipelines at scale," said Luis Maldonado, VP of Product at dbt Labs. "These new features take this even further, significantly improving data workflows and AI workloads, all while empowering more users with powerful business insights."

Improve the way data teams build, test, and ship high-quality data pipelines
dbt Cloud includes a host of enterprise capabilities for delivering trusted data quickly, securely, and affordably. New features include:

• dbt Assist: An AI-powered copilot experience, dbt Assist automatically generates documentation and tests to let data developers get more done in less time (beta).
• Advanced CI: A new "compare changes" view lets teams verify that changes to the codebase meet quality expectations before they are merged into production (beta coming soon).
• Unit testing: A new feature that allows teams to improve test coverage without driving up data platform spend through earlier validation of modeling logic (generally available).
• dbt Cloud CLI: Offers developers the flexibility to contribute to projects in dbt Cloud through their terminal or IDE of choice (generally available).

Seamlessly trace and orchestrate data pipelines in more platforms
Data teams rely on dbt Cloud as a control plane to catalog, orchestrate, govern, and observe their end-to-end data workflows. New platform enhancements and integrations give dbt Cloud even more context into the dashboards and decisions that dbt models power. New capabilities include:

• Automatic exposures: Gives dbt Cloud automatic awareness of Tableau dashboards downstream of dbt models, allowing users to trace and automate end-to-end data lineage to unlock efficiencies, optimize compute costs, and improve data freshness and trust. Auto-exposures are incorporated throughout dbt Cloud including in dbt Explorer, orchestration workflows, and CI jobs (beta coming soon).
• Microsoft integrations: dbt Cloud now supports Microsoft Azure Synapse (preview) and Microsoft Fabric (generally available).

Empower more stakeholders to collaborate in the data workflow
In order for data to be a true competitive advantage, it needs to be accessible across the organization. Stakeholders—of various technical aptitudes—now have more avenues to engage with dbt Cloud to build, improve, and trust the outputs of data workflows. This is made possible through:

• Low-code development experience: A drag-and-drop visual editor that generates SQL, which lowers the barrier to entry for more contributors to collaborate on the analytics engineering workflow in dbt Cloud (beta).
• dbt Explorer enhancements: Introduced at Coalesce 2023, dbt Explorer is an intuitive, interactive catalog for data teams to understand, improve, and troubleshoot their dbt assets across teams and projects (existing capabilities, including column-level lineage, now generally available). Soon, users can do more with dbt Explorer using enriched lineage and auto-exposures, telemetry into model consumption to align development work with business impact, and embedded data health signals in analytics tools for trusted data delivery at scale (all beta coming soon).
• dbt Semantic Layer enhancements: Includes enterprise-critical features such as granular access controls and permissions (preview coming soon), Tableau and Google Sheets integrations (generally available), declarative caching (generally available), and improvements to MetricFlow that allow teams to build and consume complex metrics with more velocity and accuracy (generally available).
• Multi-project support: Allows organizations to manage complexity by supporting multiple inter-connected dbt projects aligned to individual business domains, instead of a single monolithic project. Support for this pattern, known as dbt Mesh, is now generally available.

"dbt Cloud allows us to take all the data we've collected and actually make it useful to the business," said Evan Cover, Director BI Engineering & Governance at Klaviyo. "With dbt Cloud at the center of our transformation workflows, we can build data products that represent the reality of our business objectives and model how we go about selling, attracting, marketing, and retaining customers. By enabling more people across the business to collaborate on building trusted data products, dbt Cloud allows us to work faster, more efficiently, and take more advantage of our data."

As a trusted platform for mission-critical data workloads, dbt Labs continues to invest in the performance, reliability, and scalability of dbt Cloud. Recent dbt Cloud platform improvements include:

• Managed dbt versions: With no manual dbt upgrades required, dbt Cloud users have early and continuous access to vetted features and fixes as they become available (generally available).
• Performance improvements: Now offers significantly improved parse performance, solidifying dbt Cloud as the most performant way to run dbt (generally available).
• Microsoft Azure support: dbt Cloud will soon be available on Microsoft Azure (beta coming soon).  

For more information on dbt Cloud, visit: https://www.getdbt.com/product/dbt-cloud

About dbt Labs
Since 2016, dbt Labs has been on a mission to help analysts create and disseminate organizational knowledge. dbt Labs pioneered the practice of analytics engineering, built the primary tool in the analytics engineering toolbox, and has been fortunate enough to see a fantastic community coalesce to help push the boundaries of the analytics engineering workflow. Today there are 40,000 companies using dbt every week.

To learn more about dbt Labs, visit getdbt.com and follow us on LinkedIn.

SOURCE dbt Labs