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DBT Labs unveils AI assistant, more tools to transform data

The data transformation specialist's latest update includes new governance and metadata management capabilities as well as an AI-powered copilot for developer efficiency.

DBT Labs introduced a series of new tools designed to help customers transform data to use in informing models and applications, including an AI-powered assistant and added capabilities in its semantic layer.

Among other additions, the DBT Cloud update released on May 14 includes a low-code development experience, a feature that enables users to improve testing during the development process and new integrations with Microsoft.

Together, the new features demonstrate that DBT Labs is expanding beyond the semantic modeling capabilities that first attracted users, according to Doug Henschen, an analyst at Constellation Research. By adding AI-powered capabilities, low-code features and more governance, the vendor is not merely adding new tools, but making its platform usable by a broader audience than just expert coders.

It's clear DBT Labs is expanding into deeper governance and metadata management and into supporting a broader base of users through AI assistance and low-code/no-code capabilities for not-so-SQL-savvy users.
Doug HenschenAnalyst, Constellation Research

"This set of announcements is significant," Henschen said. "It's clear DBT Labs is expanding into deeper governance and metadata management and into supporting a broader base of users through AI assistance and low-code/no-code capabilities for not-so-SQL-savvy users."

Based in Philadelphia, DBT Labs -- whose name stands for "data build tool" -- began as a developer of an open source set of capabilities aimed at helping engineers transform raw data into a cleansed and validated form that can be joined with other data to inform analysis.

The vendor still provides an open source version of its platform at no cost, but it is now a for-profit vendor as well, offering a Team version of its tools for $100 per developer seat, per month, and an Enterprise version with customized pricing.

Now, given the DBT Cloud features the vendor introduced -- some of which are generally available and some of which are in beta testing or preview -- DBT Labs is demonstrating plans to evolve and grow, according to Henschen. However, amid that growth, the vendor might find new competition from metadata management and data catalog vendors.

"DBT Labs has been one of those no-nonsense vendors that won adoption and a loyal fan base among data professionals," Henschen said. "From that strong base, DBT Labs is now expanding into adjacent areas that are a natural fit for its customers, but where other solutions ... have been available. It will be interesting to see how far it can go."

New capabilities

Interest in enterprise AI, fueled by the rise of generative AI (GenAI), has exploded in the 18 months since OpenAI's launch of ChatGPT marked a significant advance in the capabilities of large language models (LLMs).

Enterprises and vendors alike have long searched for ways to enable more employees to use data to inform their work as well as make experienced data users more efficient. Natural language processing (NLP) and low-code/no-code approaches held promise, but never fully delivered.

Generative AI might finally be the technology that lets more users work with data and helps experts be more productive.

LLMs have extensive vocabularies, which enables true NLP and greatly reduces the need to write code to interact with software systems. In addition, LLMs can infer intent, further enabling users to engage with technology using free-form language.

As a result, when combined with an organization's proprietary data, generative AI enables virtually any user to generate insights that can inform business-specific decisions.

Given that potential, many data management and analytics vendors have made generative AI a prominent part of their product development plans. In particular, they have unveiled plans to develop AI assistants that enable natural language interactions and simplify tasks.

Now, DBT Labs is joining the fray.

The vendor's DBT Assist is an AI-powered assistant that focuses on improving the productivity of data experts rather than providing conversational capabilities. Currently in beta testing, the tool automatically provides documentation and generates tests to eliminate some of the manual labor required during the development process and help improve efficiency as developers build applications.

As helpful as the tool might be, equally important is simply providing an AI assistant to remain competitive with other vendors, according to Kevin Petrie, an analyst at BARC U.S. By unveiling generative AI capabilities, whether in general availability like a small group of vendors have or in development like most others, DBT Labs is showing customers that it is up on the latest technology.

"The GenAI copilot, while not a new concept, is critical to remaining competitive in this space," Petrie said. "DBT Assist is in beta, which reflects caution. Many vendors are taking time to incorporate user feedback on these features before going [generally available]."

In addition to DBT Assist -- and perhaps even more significant, given that AI assistants are becoming common -- the new governance, metadata management and low-code development experience features are significant additions to the vendor's platform, according to Henschen.

New governance features added to the DBT Semantic Layer include granular access controls and permissions, integrations with Tableau and Google Sheets, and improvements to MetricFlow that help users develop and consume complex metrics with greater speed and accuracy. All but the access controls and permissions, which will soon be in preview, are now generally available.

New metadata management features in DBT Explorer add improved data lineage capabilities, model performance monitoring to better match the amount of work needed to build a model with its business impact, and data quality information embedded in analytics tools to ensure data can be trusted. All will be in beta testing soon.

The low-code development experience, now in beta testing, includes a drag-and-drop editor that generates SQL code on its own, enabling users who don't know how to write code to collaborate during the data engineering process.

"All vendors seem to be adding GenAI assistants, so DBT Assist seems less significant to me than the combination of governance, metadata management and democratization upgrades," Henschen said.

Petrie likewise noted that the new governance features in the DBT Semantic Layer are important and show DBT Labs' growth beyond its roots.

With many enterprises struggling to manage data across various departments, tools that help keep data consistent and organized are imperative.

"The semantic layer enhancements also improve DBT's competitive position," he said. "So many companies struggle to keep distributed consumers of distributed data sets on the same page. The semantic layer helps govern this data consumption with consistent metrics, access controls and usage patterns."

Additional new capabilities include the following:

  • A view during data pipeline development that lets teams view and verify changes to the codebase to make sure the changes meet quality standards before they are put into production; beta testing is coming soon.
  • Unit testing, a feature now generally available that improves the coverage of tests during development without increasing costs by validating model logic earlier in the development process than was previously available.
  • DBT Cloud CLI, a tool now generally available that provides developers the option of contributing to projects in DBT Cloud through their integrated development environment of choice rather than forcing them to develop withing DBT's environment.
  • The availability of DBT Cloud on Microsoft Azure, with beta testing coming soon, plus integrations with Azure Synapse, in preview, and Microsoft Fabric, generally available.
  • Automatic exposures, a feature scheduled for beta testing soon that enables users to automate and trace end-to-end data lineage between Tableau dashboards and DBT models to help users trust the data populating their data visualizations.
  • The general availability of DBT Mesh, a feature that enables organizations to manage interconnected projects across domains rather than require them to centrally manage all projects.

While each of the new features addresses different user needs, together they are aimed at enabling DBT Labs customers to efficiently develop data pipelines, Petrie noted.

That is significant because data pipelines are what feed analytics and AI applications.

"BARC research shows that companies' No. 1 success metric for AI is the timeliness and accuracy of insights, which depend directly on the effectiveness of data pipelines," Petrie said. "DBT's enhancements help companies meet increasingly rigorous requirements for analytics initiatives, especially those that involve AI."

Feedback from companies, meanwhile, served as part of the impetus for the latest DBT Cloud update, according to Luis Maldonado, vice president of product at DBT Labs.

In addition, the vendor's own observations of trends and ideas about how users can best be helped as they transform data for analysis were part of the development process, he continued.

"We certainly listen to direct feedback, but have also observed that our customers are seeing an increasing importance in aligning data across the entire organization," Maldonado said, noting the updates to the DBT Semantic Layer. "The next step is to make it even easier for stakeholders of various technical aptitudes to connect and collaborate around their data."

The road ahead

As DBT Labs plans future updates, generative AI will be a primary focus, according to Maldonado.

In recent research conducted by the vendor, one-third of respondents said they are currently using generative AI. However, more than half expect to be doing so soon. To meet the needs of those customers that want to add or expand their use of generative AI, DBT Labs' roadmap includes data transformation capabilities to feed tools such as conversational assistants trained on customers' proprietary data.

"Many customers want ... the ability to ask questions of their internal data using LLMs, but recognize the operational risk of powering responses with inaccurate data," Maldonado said. "As we continue to embrace GenAI, we're dedicated to ensuring the data that feeds into these models is accurate and trustworthy."

Henschen, meanwhile, noted that DBT Assist has limited capabilities compared with some of the AI assistants developed by other vendors. Unlike other copilots that have conversational capabilities, it focuses largely on automated documentation.

As a result, DBT Labs would be wise to add more functionality to DBT Assist or develop another generative AI tool as a complement, he said.

"I think [DBT Labs] is headed in the right direction on many fronts, but the functionality of DBT Assist is rather limited," Henschen said. "I could easily see more assistants or a more broadly capable single assistant that would help with the company's push to support a broader base of users."

Eric Avidon is a senior news writer for TechTarget Editorial and a journalist with more than 25 years of experience. He covers analytics and data management.

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