DBT Labs acquires Transform to enhance Semantic Layer tool

Expanding capabilities

Given its focus on transforming data, DBT Labs has formed numerous partnerships with data management and analytics vendors that enable joint users to more easily prepare their data for analysis. In December 2022, it partnered with ThoughtSpot; other partners include Alation, Sigma Computing and Veezoo.

In February 2022, DBT Labs raised $222 million in series D funding, bringing its total funding to $414.4 million. At the time of the funding, Handy said he wanted DBT Labs to become the standard semantic layer for analytics.

Eight months later, DBT Labs launched the public preview of Semantic Layer, a tool built to enable organizations to centrally define their business metrics in the DBT Labs platform and then query and analyze those metrics using the analytics tool of their choice.

With the tool, DBT Labs allows users to organize their data and make it more consistent for self-service use, said Wayne Eckerson, founder and principal consultant at Eckerson Group.

"A semantic layer creates a business view of data, shielding users from having to know SQL or how to query and join multiple tables or sources." he said. "It also enables data architects to model and centralize core business constructs such as metrics and [key performance indicators] so everyone uses the same logic, improving data consistency."

Semantic layers can be a pivotal way to enable broader access to data, making it easier for self-service business users to access data and take action so they don't have to rely on centralized teams to parse out information, explained Mike Leone, a senior analyst with Enterprise Strategy Group.

The importance of a semantic layer goes a long way in bridging the gap between disparate data sources and data-centric applications.

"As organizations continue to prioritize data democratization, the importance of a semantic layer goes a long way in bridging the gap between disparate data sources and data-centric applications used throughout the business," he said.

Beyond DBT Labs, other vendors offering semantic modeling capabilities include Looker -- now owned by Google -- and MicroStrategy.

The combination of Transform's MetricFlow and DBT's tool provides a way for businesses to standardize data access and provide a unified, trusted view of data to the wider business, Leone said.

"That means greater data integrity, accuracy, performance and faster time to insight," he added.

Handy noted that as an open source vendor, DBT Labs unveils capabilities to the public in their nascent form and subsequently develops them over time. That was the case with Semantic Layer, he continued.

The tool, which is still in public preview, has its early adopters but will take years of development before it reaches full functionality. And one of the basic capabilities it lacked when first launched was support for joins.

"Semantic Layer is still a work in progress," Handy said. "We consider it one of the most important things we'll work on over the next five years. It's still very much in the beginning of its life."

He added that DBT Labs held off developing its own functionality to support joins in relational databases, given its complexity.

"We knew that whenever we tackled that problem, we needed to be really ready to invest there," Handy said. "Transform's capabilities in that area are going to be a natural puzzle piece that fits with what we already do."

Future plans

DBT Labs has no immediate plans to make another acquisition, according to Handy.

He noted that the company had opportunities in the past but takes a cautious approach given the complexities -- and risk of failure -- inherent in any acquisition.

"The market is encouraging companies to focus on what they already do well, so that's our focus in the next 12 months," Handy said.

Beyond adding functionality to Semantic Layer, DBT Labs' roadmap includes a focus on enabling teams within organizations to work more efficiently with one another.

Data mesh is a growing trend in analytics, with organizations removing data stewardship from a centralized team and federating it across teams and departments. The intent is to remove bottlenecks while taking advantage of the domain expertise of data workers in specific teams and departments.

Enabling data workers to share and collaborate across teams and departments, however, can be challenging.

"The complexity of use cases has risen significantly over time, and there's a whole class of things we need to build in order to enable this increasing complexity," Handy said.

Eckerson, meanwhile, said he'd like to see DBT Labs address ease of use. While its tools are attractive to data engineers, they still require coding.

At a certain point, that could hinder DBT Labs' growth, making it vulnerable to alternative vendors, according to Eckerson. For example, Coalesce is a data transformation vendor that offers no-code/low-code tools that Eckerson likened to modern data warehouse automation.

Therefore, DBT Labs could benefit from adding no-code/low-code options for potential users that don't possess coding knowledge.

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