In 2000, some companies had big data problems before the concept we know today even existed. Back in those days, these organizations used a concept called the data warehouse to deal with large amounts of data, and a company called Netezza formed to build an appliance to simplify data warehousing. The company was sold in 2010 to IBM for a cool $1.7B, but they have decided to get the band back together to solve today’s big data problems in a more modern context –and today they came out of stealth to announce Series A funding.
The new company called Cazena is based in Waltham, MA and they are getting $8M from Andreessen Horowitz and North Bridge Venture Partners to pursue this new vision. While CEO Prat Moghe was thin on details about the new product, which is still in development, the funders were willing to take a chance with a team with a billion dollar track record and a willingness to rethink the big data problem.
As Moghe told me, when they were solving the issue of dealing with large sets of data the first time around with Netezza, it involved building an appliance with storage, compute power and networking in a single package. In the 2000 timeframe that reduced the cycle for building an analytics system from years to months, but in today’s on-demand world, even months is far too long.
“Our vision is to see if we could simplify and speed up access to data. You would not need months. It would take hours, almost like data on demand,” Moghe told me. With today’s tools that’s certainly possible. It just remains to be seen how Cazena hopes to do it.
Moghe did tell me this much. He said we have all these technologies like Hadoop, Spark and NoSQL and companies are investing millions of dollars implementing them, but to be truly effective these tools have to filter down to the line of business. “We don’t believe [these technologies alone] are solving the problem and making big data more consumable for lines of business,” he told me. That’s why Cazena is designing a new layer that sits on top of all this big data technology that figures out how to get data to you faster in the right form without requiring an expert to help you understand all of the back-end processing bits.
He also hinted that the solution involves the cloud because cloud elasticity and scalability is helpful when it comes to dealing with lots of data. But he also said, moving large amounts of data to the cloud brings a host of issues such as security and network bandwidth they hope to solve.
Moghe pointed out that today 90 percent of the Fortune 2000 companies are still processing big data on-premises and Cazena wants to change that by building a console. “You have data and you can begin [processing] it in a single click and it’s available for analysis in an hour,” he said. At least that’s the vision.
Cazena hopes to reduce the complexity involved in big data and automate a large portion of the difficult parts of dealing with it. It’s a vision that makes a lot of sense if they can pull it off. Big data requires a substantial amount of storage and compute power and different jobs could require assorted software for optimal processing. Users don’t necessarily need to understand all of this. They just want to find and make sense of the data without worrying about the rest.
The first solution should be available in early 2015 and we will learn more then.
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