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Is 2014 the Year of the 'Big Data Stack'?

Thor Olavsrud | Jan. 10, 2014
There is a dizzying array of big data reference architectures available today. 2014 may be the year we see a big data stack—similar to the LAMP stack that drove development of dynamic and interactive websites in the dotcom era—begin to coalesce.

One step further along is prescriptive analytics, sometimes considered the holy grail of business analytics, which takes those predictions and offers suggestions for ways to take advantage of future opportunities or mitigate future risks, along with the implications of the various options.

"You have to go through and do predictive to get value out of big data," he says. "It's a low likelihood that you're going to get a lot of value out of just slicing and dicing data. You've got to go all the way up the stack."

"At least 70, maybe even 80 percent of what we see around big data applications is now predictive or even prescriptive analytics," Daley adds. "That's necessity, they mother of invention. It starts at the bottom with data technology—storage, data manipulation, transformations, basic analytics. But what's happening more and more, finally, is predictive, advanced analytics is coming of age. It's becoming more and more mainstream."

While predictive analytics are somewhat mature, it's currently an area only data scientists are equipped to handle.

"I think predictive is a lot farther along than the bottom layer of the stack," Daley says. "From a technology standpoint, I think it's mature. But we need to figure out how to get it into the hands of a lot more users. We need to build it into apps that business users can access versus just data scientists."

What's That Spell? DIAP? PAID?
Call it the DIAP stack. Or maybe start from the top and call it the PAID stack. The trick now, Daley says, is not just adding more maturity to component technologies like Hadoop and NoSQL, it's providing integration up and down the stack.

"That's a very key point," he says. "To date, all these things are separate. A lot of companies only do one of these things. Hortonworks will only do the data side, they won't do integration, for example. But customers like to go through and buy an integrated stack. We should at least make sure that our products up and down those stacks are truly integrated. That's where it's going to have to get to. In order to really get adopted, products and vendors are going to need to work up and down that stack. I need to support every flavor of Hadoop—at least the commercially favorable ones. And it's the same thing for NoSQL."

 

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