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The market for and demand for technologies that help businesses use cloud data lakes effectively are growing quickly. Upsolver is one of the companies in this field that is growing quickly. On Tuesday, it announced that it had raised $25 million in a Series B round of funding.
Scale Venture Partners led the funding round, and JVP, Vertex Ventures US, and Wing Venture Capital also took part. The new money comes less than a year after the $13 million Series A funding that Upsolver got in June 2020.
Upsolver’s cloud data lake-enabling technology gives users a platform that doesn’t require coding to change data so that it can be queried and used to help make decisions. The promise of no-code is a drag-and-drop interface that lets users set up complicated settings without having to write code.
In this Q&A, Ori Rafael, CEO, and co-founder of the Sunnyvale, California-based company Upsolver, talks about the cloud data lake market and what data engineering is all about.
Why are you now trying to raise money for Upsolver’s Series B?
Ori Rafael: We didn’t expect to raise more money so soon after our Series A round, which ended in June 2020. But it was a very good year for growth, so we decided to raise a lot sooner than we had planned.
We are pretty much growing on all fronts. We’re going to hire more people and bring in the ones we need to help us grow. I think it’s about getting things done faster. That was the point of the round, along with making the team stronger.
How has Upsolver’s business changed since you helped to start it in 2014?
Rafael: When we first started Upsolver, we were in the advertising business. Eventually, we built our own advertising database. Three years ago, we changed the direction of the company by using the product we made ourselves to make iterations faster-using data lakes.
From a business point of view, I think it’s really amazing that data lakes have become so popular in the last few years. When we first started, we had to tell people what a “data lake” is, but now everyone has one.
From three years ago to now, the data lake area has changed a lot. It’s amazing to see how much it has grown. Data is really in its golden age.
What is a platform for engineering in a data lake? Is it something that data middleware does?
Rafael: People tend to think of something very traditional from the business world when they hear the word “middleware.” We don’t know enough about data middleware to call it by that name.
We are in the business of turning raw data into information that people can use. It’s kind of a mix of the old world of ETL (extract, transform, load) and the world of databases.
Upsolver is a platform for data lake engineering that doesn’t need any code. Open-source platforms like Hadoop or Spark would be the best match. They also help you manage the data lake, but it’s a very code-intensive process with hundreds of configurations that requires very skilled big data engineers. Upsolver is a no-code alternative. So, we use a no-code method to give you the benefits of using a data lake.
How do you see Presto fitting into Upsolver’s data lake engineering platform?
Rafael: A Presto engine is used to ask questions about a data lake, and an Upsolver engine is used to build a data lake. Together, they make a solution that is just as useful as a database.
We work closely with different versions of Presto, such as Amazon Athena, which is the managed Presto service on AWS. Presto is a great use case for us, so we joined the Presto community and I’m on the Governing Board of the Presto Foundation.
Ahana is a Presto query engine that searches the data lake, just like Amazon Athena. So any query engine that can query the data lake is a natural partner for us.
We can already see that the data lake has a lot of query engines on top of it. So we have Trino and Presto, and there are also commercial query engines like Ahana, Starburst, Dremio, and Amazon Athena. It makes sense that there will be a lot of different query engines on the market.
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