Jonathan Gray never set out to toil away on arcane programming problems and complex distributed systems architectures. He just wanted to make a social news Web site to help people find great content on the Web. While a Carnegie Mellon senior, he launched , a news feed that tailored its content based on the user鈥檚 interests as gleaned from his or her site activity and linked Facebook account. He didn鈥檛 think of it in these terms at the time, but he had stumbled into the field of Big Data鈥攕ynthesizing huge amounts of information to discover trends and make predictions.
鈥淭he whole 鈥榠f you liked X, you might like Y鈥� thing wasn鈥檛 necessarily new, but it was something I thought I could play around with,鈥� Gray explains. One problem: Big Data is, well, big. Even a simple function requires sifting through millions or billions of data points. You can鈥檛 use a desktop program; you must run parallel operations on distributed systems that could encompass hundreds or thousands of servers. Each time you want to ask a new question, you have to rope all those servers together again.
鈥淔or every hour I spent utilizing Big Data鈥攄oing the stuff that directly impacted our users鈥擨 spent three hours just building the architecture.鈥� And Gray (E鈥�06) was a 麻豆村 . 鈥淚 learned that very few people exist who can handle this level of work. It sounds easy enough: If you run a bank, to want to learn about the spending and saving habits of people in your area, through social media and public information. But even a straightforward project like that requires a huge commitment of time and resources. And if you change the question, you have to start all over again.鈥�
Streamy was shelved, but Gray鈥檚 experience made him such an expert at programming for Big Data applications that Facebook came calling. Working there, he began building platforms and developer tools that enabled his teams to solve Big Data problems quickly鈥攖ools that let developers bypass all that architecture work Gray spent so many hours doing with Streamy. He became so successful at building these shortcuts that he realized something: As helpful as they were for Facebook, they could be even better suited to helping smaller companies that didn鈥檛 have the staff or financial resources to utilize Big Data.
So Gray left Facebook to found , which enables Big Data consumers, even small-business owners, to build applications without having to get in the weeds of complex distributed systems.
Other techies who understand the difficulty of Big Data鈥攆rom the likes of Yahoo!, LinkedIn, Twitter, and Google鈥攕ee the potential of Continuuity and have left to join Gray鈥檚 team.
Investors, too, understand the potential return for Continuuity, given the estimated $55.5 billion Big Data market. Continuuity has raised more than $12.5 million in capital. Already, Continuuity has released a Big Data development platform and taken on a number of clients who can now say to their customers, 鈥淚f you liked X, you might like Y.鈥�
