Big data systems tend to be large, complex and often hard to troubleshoot. In the world of databases, web and mobile stacks, application performance management services like AppDynamics and New Relic help ops teams keep tabs on their system. In the big data world, Unravel Data is one of the few APM players to focus solely on the complete big data stack from ingestion to analysis (though companies like Driven and others are also working on at least some aspects of this).
Unravel today announced that it has raised a $15 million Series B round led by GGV Capital, with participation from Menlo Ventures and Microsoft Ventures. This brings Unravel’s total funding to $23 million.
Kunal Agarwal, Unravel’s CEO, tells me that the company, which was founded in 2013, used 2017 to find its product/market fit after its Menlo Ventures-led Series A round in 2016. With a number of prestigious customers on board (including Kaiser Permanente, Autodesk and YP.com), Unravel is now planning to expand its sales and marketing teams to help bring on new customers and support its existing user base. Unsurprisingly, the company will also continue to invest in building out its core product, and the company also plans to open an office in London where many of its customers in the financial industry (still) have their headquarters.
“Last year was the product/market fit year. This year is the breakout year,” Agarwal told me. “In 2017, we were focused on getting a certain number of revenue and customers.”
Unravel’s systems gather their data both from the servers and containers that run the big data stack and from the applications directly. What makes the service stand out, though, is that it covers the complete stack from Hadoop and Spark to Kafka and the Impala query engine for Hadoop. Using the data it gathers, the service allows you to monitor the performance of your stack, but maybe most importantly, Unravel also provides suggestions for improving your setup and can even automatically apply them.
The big data market keeps evolving quickly and, like its competitors, Unravel will have to stay ahead of the curve. Looking ahead, Agarwal expects that his users will want to do more real-time processing and many of them are also now dipping their toes into machine learnings. Those use cases present their own challenges, but in terms of APM, they are a good fit for a company like Unravel, given that they are essentially big data challenges, too.
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