Hadoop

Why Hadoop Only Solves a Third of the Growing Pains for Big Data

28th Jan `14, 08:11 AM in Hadoop

Apache Hadoop has addressed two of the growing pains organizations face as they attempt to make sense of…

BDMS
Guest Contributor
 

Apache Hadoop has addressed two of the growing pains organizations face as they attempt to make sense of larger and larger sets of data in order to out-innovate their competition, but four more remain unaddressed. The lesson: Just because you can avoid designing an end-to-end data supply chain when you start storing data doesn’t mean you should. Architecture matters. Having a plan to reduce the cost of getting answers and simultaneously scale its utility to the broader organization means adding new elements to Hadoop. Fortunately, the market is addressing this need.

Traditional databases were not the repository we needed for big data, 80 percent of which is unstructured. Hadoop offered us, for the first time, the ability to keep all the data in a single repository, addressing the first big data growing pain. Hadoop is becoming a bit bucket that can store absolutely everything: tabular data, machine data, documents, whatever. In most ways, this is a great thing because data becomes more valuable when it is combined with other data, just like an alloy of two metals can create a substance that is stronger and more resilient. Having lots of different types of data in one repository is a huge long-term win.

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