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Hadoop is not enough for Big Data, says Facebook analytics chief

Hadoop is not enough for Big Data, says Facebook analytics chief

Don't discount the value of relational database technology, Ken Rudin told a Big Data conference

The Hadoop programming framework may be synonymous with the Big Data movement but it's not the only tool companies need to derive insights from massive stores of unstructured information, according to Facebook analytics chief, Ken Rudin.

"There are a lot of commonly held beliefs about big data that need to be challenged," with the first being that you simply adopt Hadoop and are good to go, Rudin said during a keynote at the Strata + Hadoop World conference in New York. "The problem is that Hadoop is a technology, and big data isn't about technology. Big Data is about business needs."

"In reality, Big Data should include Hadoop and relational [databases] and any other technology that is suitable for the task at hand," he added.

Facebook's business model depends on the way it crunches the streams of profile and activity data generated by the social media site's more than 1 billion users in order to deliver targeted advertisements. But "Hadoop isn't always the best tool for what we need to do," Rudin said.

For example, it makes sense to do broad exploratory analysis of a data set in Hadoop, but a relational store is better for conducting an operational analysis of what was uncovered, he said.

Hadoop is also good for looking at the lowest level of detail in a data set, but relational databases make more sense for storing transformed and aggregated data, Rudin added.

"The bottom line is, use the right technology for whatever it is you need," he said.

There's also the presumption that the mere act of analyzing big data provides valuable insights, Rudin said. "The problem is coming up with more brilliant answers to questions nobody cares about," he said. "It is still an art to figure out what the right questions are."

Facebook has focused on hiring the right staffers to run its analytics operations, people who not only have doctorates in statistics but are also business-savvy, he said.

"When you interview [candidates], don't focus just on, 'how do we calculate this metric,'" Rudin said. Instead, give them a business case study and ask them what would be the most important metrics to look at, he added.

Companies should also attempt to train "everyone on analytics", according to Rudin.

Facebook runs an internal "data camp," a two-week program that teaches employees about analytics. Product managers, designers, engineers and even finance department workers attend, Rudin said. "The value of having everybody go through it is, you give everybody a common language of data they can discuss problems and issues with," he said.

Facebook has also shaken up the way it organizes statisticians and business teams. If statisticians are kept separate, they tend to "sit there and wait for requests to come in from areas of the business and respond to them," instead of being proactive, he said.

But if statisticians are placed into business units, ""you'll find multiple groups trying to solve problems redundantly," he said.

Facebook has settled on an "embedded" model, wherein analysts are placed with business teams but report up to a higher-level group of analysts, which helps avoid duplicate efforts.

Chris Kanaracus covers enterprise software and general technology breaking news for The IDG News Service. Chris' email address is Chris_Kanaracus@idg.com

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