2 Data Lake Unicorns Raise $235M

Data lake unicorns Dremio and Starburst Data raised hundreds of millions of dollars in late-stage funding today. Data lake unicorns Dremio and Starburst Data raised hundreds of millions of dollars in late-stage funding today. Dremio, a data analytics storage startup, closed a $135 million series D round, lifting its valuation to an even $1 billion and into unicorn status. Meanwhile, Starburst Data, an analytics software provider, scored a $100 million Series C funding round bringing its valuation to $1.2 billion. The massive late-stage investments, coupled with both startups’ year-over-year revenue growth, reflect the growing importance of big data technology and analytics as enterprises struggle to extract in-depth insights from growing volumes of data across departmental silos, mainframes, and legacy systems. And it also indicates that these types of cloud-delivered storage and analytics services will continue growing in value through 2021. Dremio’s funding round was led by Sapphire Ventures with participation from existing investors Insight Partners, Lightspeed Ventures, Norwest Venture Partners, Redpoint Ventures, and Cisco Investments. To date, Dremio has raised $247 million in six funding rounds, and the series D comes nine months after a $70 million round during which time the company doubled its customer count, employee base and revenue.  CEO Billy Bosworth said the new financing serves as “validation of the rise of cloud data lakes as an open, flexible, scalable architecture for analytics.” The company plans to use the funding to invest in engineering and grow employee headcount from customer-facing teams to back-office support. Headquartered in Santa Clara, California Dremio, founded in 2015 by former MapR employees Shiran and Jacques Nadeau, offers an analytics service called Data Lake Engine. It provides fast query speeds and a self-service semantic layer that operates directly against data lake storage.  Dremio is built on open source technologies including Apache Arrow and Apache Arrow Flight, which the company co-created to provide columnar, in-memory data representation and sharing.  According to co-founder Tomer Shiran, Dremio enables business analysts and data scientists to explore analyze any data in a self-service fashion at any time, regardless of location, size, or structure, using their preferred tools such as Tableau, Python, and R.  The 3-year-old, Boston, Massachusetts-based company has raised more than $164 million in the last 12 months. The latest VC haul, led by Andreessen Horowitz with participation from Salesforce Ventures as well as existing investors Coatue and Index Ventures, triples the startup’s valuation from $342 million last June. Starburst was founded by CEO Justin Borgman, the former co-founder and CEO of Hadapt, the “SQL-on-Hadoop” company, in 2017 as a spinoff after Hadapt was acquired by Teradata in 2014. However, Starburst has since shifted its focus from Hadoop to Presto, an open-source query project, that serves as the engine behind the startup’s enterprise distribution platform. “Digital transformation has become an operational requirement, organizations are relying on data-driven insights to develop a competitive advantage, reduce costs, and more quickly identify new opportunities,” Borgman said. “Unfortunately, even with millions of dollars invested in expensive data management tools, most organizations are still making decisions that are often too slow or based on incomplete, irrelevant data. Investors are taking note and backing solutions that help to solve that problem.” Borgman said the funding round will be used for product development and global expansion. Today, more data crosses the internet every second than was stored in the entire internet just 20 years ago, and how that data is collected, accessed, and analyzed can determine whether a business sinks or swims.  But with data dispersed across multiple systems, accessing data for analysis to make data-driven decisions is becoming more difficult. “The old method of moving all data to a “single source of truth” is slow and isn’t built for the volume of data we have today,” Borgman said. “Data engineers are spending half of their time moving data, leaving data scientists and business analysts waiting.  Cloud data lakes and data warehouses are going to have a big year, or perhaps be big even for years to come, as companies continue to mix and mingle the two, and query compatibility will be an absolute must.