Fast-growing data management startup dbt Labs Inc. today said it has raised $222 million in funding from a group of prominent backers. 

Snowflake Inc., Databricks Inc., Alphabet Inc.’s GV fund and the venture capital arm of Salesforce.com Inc. were among the participants in the round. Altimeter was the lead investor. Andreessen Horowitz, Sequoia Capital, Coatue, Tiger Global, ICONIQ Growth and GIC contributed as well. 

The funding round values dbt Labs at $4.2 billion. The startup will use the newly raised capital to hire 300 more employees over the next year, Forbes reported. 

Philadelphia-based dbt Labs is the developer of dbt, a popular data transformation tool. Data transformation is the process of changing a dataset to make it easier to process. For example, if a company has several spreadsheets’ worth of information about the effectiveness of a recent ad campaign, it might consolidate the spreadsheets into a single file to make analysis easier.

Data transformation initiatives can also have other objectives. A company may seek to filter inaccuracies from a dataset, change some of the individual data points or update the file’s format. 

Workers at companies including the operator of the Nasdaq stock exchange and Canva Inc. use dbt to help them carry out data transformations. The tool can be deployed on data platforms such as Snowflake, Databricks and Google BigQuery. It provides the ability to write data transformation workflows using the SQL syntax, which many developers are familiar with and is considered relatively easy to use. 

Another key selling point is that dbt helps ensure data transformations are carried out reliably. Users can have the tool automatically check the results of a data transformation for potential errors. A user could, for example, configure dbt to detect if a product description is accidentally added to a database column designed to store product prices. 

A version control feature allows companies to track changes to a data transformation and restore a previous version if technical issues emerge. 

After users create a data transformation workflow with dbt, they have multiple ways of implementing it. The tool includes optimization options that help reduce the amount of time necessary to run workflows. One of those options allows users to ensure that a data transformation isn’t applied to records to which it was already applied once before, which avoids unnecessary computations and speeds up processing.

“The speed, scalability and expressiveness of modern SQL paired with the modern cloud data platform are stunning,” dbt Labs founder and Chief Executive Officer Tristan Handy wrote in a blog post today. “The ever-evolving SQL language itself and the cloud architecture behind these modern data platforms are changing how data tools are built and how data work is done. What data practitioners need to harness these new capabilities is a programming framework that extends and enhances SQL. That programming framework is dbt.”

More than 25,000 data scientists and about 9,000 companies use the tool, dbt Labs disclosed today on the occasion of its funding round. The startup’s annual recurring revenue increased by a factor of six last year, while the number of partner products that support dbt doubled.

Image: dbt Labs