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Big Data 101

When Christophe Bisciglia was still at Google, interviewing student engineers for admission to the Chocolate Factory, he was struck by how difficult it was for the uninitiated to grasp the company's multi-terrabyte data transformations.

"I started notice repeating pattern when interviewing students," he tells The Reg. "I would say 'OK, that's a great solution to the problem, but what would you do if you had a 1000 times as much data?' And they would just stare out at me, blank. It wasn't that they weren't smart or talented. It's just they'd never had the exposure."

In the hopes of shrinking this education gap, Google sent Bisciglia back to his alma mater, the University of Washington, where he taught a course on "working with big data." And Hadoop was the teaching model.

Google ended up hiring about half the students who took the class. And after the company open-sourced the curriculum, the same course was picked up by several other universities, including MIT and Berkeley. "In the past, it took three to six months to get hires up to speed with how to work with [Google] technology," Bisciglia says. "But if schools are teaching this as part of the standard undergraduate curriculum, Google saved that three to six months - multiplied by thousands of engineers."

To further facilitate such education, the company setup a Hadoop cluster inside one of its (then top secret) data centers, offering access to researchers across the planet.

Yes, this also juices the Yahoo!s and the Microsofts of the world. But Google is fond of saying "what's good for the internet is good for Google."

"As a result of having this large-scale data-processing technology easily available in open-source form, it makes it easier for other business to create and publish more data," ex-Googler Christophe Bisciglia says. "The more data that other business create and publish, the more data Google can slurp up and make universally accessible and useful."

Why didn't Google just open-source MapReduce and GFS on its own? Bisciglia says the company mulled the idea "a little bit," but decided it was less than practical. "MapReduce and GFS is infinitely integrated with so many other systems. Trying to cleanly excise them would be a software engineering challenge that would take millions of man hours. There would be no clean way to cut it out."

Plus, by the time Google got around to its mulling, Hadoop was already a thriving open source project. "It had a good community around it. It was seeing adoption at Yahoo! and Facebook," he says. "It wouldn't have been good for the community to have these two competing projects that do the same thing."

And, Bisciglia acknowledges, Google likes the fact that it's internal platform is "just a little bit better."

Last year, Hadoop researchers set an record on Jim Gray's sort algorithm, sorting a terabyte of random data in three minutes across 900 machines. But shortly thereafter, Google couldn't help but pipe up with the claim that it's very own MapReduce had done the job in just 60 seconds.

When it comes time to praise itself, Google isn't above lifting the code the secrecy. ®

Gartner critical capabilities for enterprise endpoint backup

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