Starburst Valuation Climbs To $3.35B With Latest Funding Round

Big data analytics startup debuts additions to its Starburst Enterprise platform for implementing distributed “data mesh” management systems, and building and sharing data products.

Data analytics platform developer Starburst has raised $250 million in a Series D round of funding that nearly triples the Boston-based company’s valuation to $3.35 billion.

The funding news on Wednesday comes as the company, which is holding its Datanova conference this week, also unveils new capabilities in its flagship Starburst Enterprise software, including “Data Products” functionality for curating and sharing data throughout an organization – even if the data resides in multiple sources.

The announcements follow a busy 2021 for Starburst. “We finished up last year with almost 3X revenue from the year before. So really strong growth,” said CEO Justin Borgman said in an interview with CRN.

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Also in 2021, the company completed a trifecta of making Starburst Galaxy, the software-as-a-service edition of the Starburst technology, available on all three leading cloud platforms (Amazon Web Services, Microsoft Azure and the Google Cloud Platform) for data lake and lakehouse analytics.

And in December the company hired Javier Molina as the company’s first chief revenue officer. Molina joined Starburst from MongoDB where he was senior vice president of worldwide sales for Atlas, the company’s multi-cloud application data platform.

The latest funding round brings Starburst’s total financing to $414 million, including a $100 million Series C round in January 2021. The latest round was led by Alkeon Capital with participation from Altimeter and B Capital Group, as well as existing investors Andreessen Horowitz, Coatue Management, Index Ventures and Salesforce Ventures.

Borgman said the funding will go toward accelerated development of its software with a particular focus on Galaxy. Starburst already has a presence across North America and Europe and the plan is to expand into Asia-Pacific this year. And Borgman said the growing company might even use some of the funding for acquisitions to extend its product portfolio.

Currently Starburst has about 350 employees with plans to add approximately 300 more this year, including development engineers and sales representatives, Borgman said.

Starburst’s data analytics platform is built on the Trino parallel processing SQL query engine and the company is a pioneer in the concept of analyzing increasingly huge volumes of data distributed across multiple locations – an alternative to the traditional approach of collecting and consolidating data in a centralized data warehouse.

“To us, that’s the reason why the classical data warehousing model doesn’t work for the future,” Borgman said, noting the trends toward increasingly decentralized data.

Borgman said Starburst’s software is being used for a range of applications, particularly in cases where disparate data from multiple sources is combined for analysis such as billing data from financial systems and customer behavior data from web sites. “Being able to combine those two data sets creates a much more holistic understanding of the customer,” Borgman said.

The financial services industry is also using Starburst’s software for analytical tasks in fraud detection and risk analysis, according to the CEO.

The new Data Products capabilities being announced today for Starburst Enterprise extend the software’s support for the “data mesh” distributed architectural approach to data management that is gaining attention across the industry. Under the data mesh concept, data ownership remains within business domains, “ensuring that data is treated as a first-class product across the organization,” according to a Starburst description, rather than a by-product of business activities.

The additions to Starburst Enterprise provide the necessary controls that form the foundation of a data mesh implementation and will allow businesses and organizations to build and share data products. The new tools, for example, help data producers and data engineers define relevant metadata for creating, publishing, finding and managing curated data products based on multiple data sets. They also provide data governance and query capabilities around data products.