Imply Data on Tuesday said it raised $100 million in a Series D round of funding to help it grow its real-time analytics database technology platform.
On March 1, the vendor introduced its Polaris database-as-a-service offering, providing a new way for users to consume Apache Druid as a managed service.
Imply is also helping to lead development on a new multi-stage query engine for Druid that will bring more powerful data query functionally to the database.
In this Q&A, Fangjin Yang, co-founder and CEO of Imply, details why he helped start the company, the challenges users face with databases today and where the technology is headed.
Why are you raising $100 million now for Imply and its analytics database technology?
Fangjin Yang: My view on fundraising is that it is a tool to accelerate what has been working at the company.
We look at what we want to accomplish on the product first and foremost, and that translates into what we want to accomplish as a company. The product and the growth of the company are linked hand in hand, and it leads to more folks that we want to bring on. We need more salespeople, more engineers, more of everyone at the company.
Fangjin YangCEO and co-founder, Imply
My personal philosophy is I'm trying to build the best company possible. If you build the best company possible and the metrics are there and the growth is there, a company should hopefully have a lot of options. In the future we'll have continued rounds of investment and an IPO at some point in the future is a very likely possibility as well.
Why did you start Apache Druid and Imply?
Yang: The reason we created Apache Druid was really because we were trying to solve a problem. Nearly a decade ago, my co-founders and I were working at a pretty small startup [Metamarkets, which was acquired by Snap], and at the time, we were trying to power a user-facing analytics application.
We started working on Druid really because we had tried a lot of databases that were available at the time and none of them were really able to meet the needs of the application that we were trying to build. So we figured since we couldn't find anything that could power our application, we had to build our own database.
We were a lot younger at the time and I don't think even then we fully realized how much work it is to build a database. But that's where we began our journey, to solve a problem. Once we built the database, we open-sourced it really because I think as engineers, we have a lot of appreciation for open source technologies and a desire to want to give back to the community.
There was never an intention to start a company. Over time we saw a lot of different types of companies, big and small, pick up the technology. There are other databases out there, but we felt we had a really good one for powering real-time analytic applications. We saw a lot of companies building different types of analytic applications with Druid and we thought, hey, this is potentially a market gap that we can form a company around. So that was the genesis of Imply.
Why is the multi-stage query engine the next big innovation in Apache Druid?
Yang: The high-level direction with the multi-stage query engine that is in development now is about unifying workflows on data processing.
One aspect of the new query engine is that it can help users to transform and manipulate data using the same query engine that's used to query data. We believe the multi-stage query engine reduces the need for people having to build complex data pipelines in order to get data into Druid.
What has been the biggest surprise for you in the time since you started Imply?
Yang: We started off Imply as three guys in this tiny apartment in San Francisco figuring out how do we start a company around some of the ideas that we had. Now fast forward a couple of years, there's a lot more people and a lot more customers want more of everything else.
I do think one of the biggest learnings for me, transitioning from a developer to more of a business leader, is around this idea of how important it is to get people to scale. I spent the first half of my career learning how to scale systems and I feel like I'm spending this part of my career learning how to scale people and an organization.