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When to use Hadoop (and when not to)

Chris Nerney | Aug. 14, 2014
Hadoop is a huge advancement in big data technology, but there are better choices for real-time analytics.

Some of the scientists who stopped using Hadoop simply may have chosen it for the wrong job -- such as real-time analytics -- in the first place. For them, moving on only makes sense.

Another potential source of dissatisfaction with Hadoop (that wasn't reflected in the Paradigm4 survey) is cost. Enterprises that go into Hadoop thinking it's going to be free or cheap because it's open source usually get a big surprise. And they usually end up paying by contracting with a Hadoop services vendor or hiring qualified Hadoop programmers and analysts to work in-house, and by then launching misguided Hadoop projects that cause them to fall behind competitors.

Early adopters of Hadoop who became disillusioned may have been victims of the first wave of Hadoop hype. The gradual maturation of big data and analytics technologies, along with better-educated customers, should make it easier for enterprises to choose the best analytics solution.

As Perlich says, "It's really about what you're trying to do that determines whether the tool is sufficient for the job."  

 

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