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Data Lake Engines - The Essential Layer of the Next Generation Data Architecture

In 2015, we partnered with two young founders to build Dremio. Tomer Shiran and Jacques Nadeau had just left MapR, and they came to work from our offices in Menlo Park. We shared a vision for a new way of working with data. Today, the company is announcing a $70M Series C to help them along that journey.

More data is being stored in data lakes like Amazon S3 and Azure Data Lake Storage. At the same time, the BI landscape has blossomed. Analysts and product managers and sales operations teams deploy Tableau, Power BI, Looker, Superset, and many other tools to parse their data. There needs to be a layer between them to make all that data accessible to these users - a data lake engine. That’s Dremio.

If you keep data in cloud data lake stores, and need a system to make that data accessible to analysis tools at interactive speed - without moving it - you’re looking for Dremio. Under the hood, a number of query acceleration technologies combine to make queries 10x+ faster. And because the system doesn’t move data, your team reduces its data analysis costs at the same time.

In the last five years, the industry has evolved the way Tomer and Jacques anticipated. O’Reilly calls this Next Architecture. Amazon operates its data lakes in this way. So do Microsoft, Pfizer, UBS, NCR, and many others.

At Redpoint, we’re passionate about innovations in data. We believe that the best companies run on data-based decision making. We wrote a book about winning with data with Frank Bien, the CEO of Looker. And we’re grateful to be a part of the Dremio journey.

Oh, and if you’re looking for a good conversation starter with a Dremio employee, ask them about the origins of their mascot, the narwhal. As the team jokes, it’s the only real unicorn.