Dremio Accelerates Growth Plans Following $25M Series B

Dremio CEO Tomer Shiran wasn’t planning on completing a Series B round of investment until later this year. Sales have been humming along for Dremio’s self-service data access tool, he says, and things were going well. But when the venture capital folks on Sand Hill Road came calling with an offer, it was too good to pass up.

“I never want to say we don’t want money, but the company was very well funded already.” Shiran tells Datanami. “We had plenty of time, and with the growth in revenue we’ve had over the last quarter and a half, it was really a question of when do you need it in the early stage of the company.”

However, it’s always a better time to raise money when things are going well, as opposed to when things are not, Shiran points out, so he relented and accepted the $25 million Series B from Norwest Venture Partners, with participation from existing investors Lightspeed Venture Partners and Redpoint Ventures. That raised the total funding for the three-year-old, Mountain View, California-based company to $40 million.

The plan calls for Dremio to use the money to expand geographically, to grow from about 35 employees to about 100 by December, and to bolster the product and the underlying technology. “It seemed like a great opportunity to put more fuel on the fire,” Shiran says. “It’s going to accelerate our growth even faster.”

The company’s pitch is centered on freeing up employees in Fortune 1000 firms to do more with their data. While Dremio doesn’t have a lot of success stories at this early stage, anecdotal evidence suggest technology buyers are receptive to the story.

The Dremio product, which is open source and accessed via Web browser, is designed to eliminate the extensive data engineering effort required to put “data consumers” in touch with data residing in Hadoop, AWS S3, and other big data repositories.

“In many companies, the folks who want to consume data, the data consumers, have their own self-service visualization tools, things like Tableau and Looker and PowerBI,” Shiran says. “But they’re always struggling to get their work done because every time they do something, they have to wait on somebody to get the data, provision it, ETL it. They’ll do engineering work for every question. It’s very frustrating for the engineering team as well.”

The Dremio product makes data simpler to access by doing several complicated things under the covers. It uses Apache Arrow in-memory technology to access a range of data sources, from HDFS and S3 to NoSQL and relational databases. It also includes native query push-downs that let users (or BI tools) send queries into any data store. Another layer called “Dremio Reflections” speeds up queries by building compressed, columnar representations of data. Lastly, it has a cost-based query optimizer, dubbed Universal Relational Algebra, that works with the Dremio Reflections to speed up access.

Customers don’t need to know how all these pieces work together, or even that they’re even there.  All that matters, according to Dremio CMO and vice president of strategy Kelly Stirman, is that it works.

“So if you’re a Tableau user, you can connect to Dremio just like connecting to Oracle or SQL Server. We look just like a relational database with respect to any tool used today,” he says. “The same for data scientists. If you’re working in Python or R or Scala, Dremio just looks like you’re connecting to a relational database. It’s very easy and fits into a way that people are used to working.”

The company isn’t trying to replace the visualization or data science engines, Stirman says. “We are the engine behind the scenes that makes the data easy to access, really fast, no matter what system it’s stored in and no matter how big it is.”

The fact that Dremio can deliver all this – as a downloadable product that runs in your Web browser, no less – may strike some frustrated data consumers as too good to be true. To hear the Dremio folks tell the story, prospects see one demo and they’re immediately ready to plug it into their data lake.

“For all the marketing and hype about data lakes and all the talk, it’s this mysterious thing that business user don’t really get to touch or appreciate,” Stirman says. “There’s lots of companies that have invested enormously in their data lakes and they just don’t have much so show for it. This is the solution to all these problems that people have been talking about for years.”

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