Ron Bodkin1 in the Hadoop and NoSQL in a Big Data Environment interview:
But then the next thing that happens is once people have started doing that level of processing they realize there is a power of being able to ask questions they never thought of before the data, they can store all the data in small samples and they can go back and have a powerful query engine, a cluster of commodity machines that lets them dig into that raw data and analyze it new ways ultimately leading to data science being able to do machine learning and being able to discover patterns in data and keep them improving and refining the data.
Arun Murthy2 quoted in PC Advisor’s Making the transition from RDBMS to Hadoop - PC Advisor:
“In the bottom-up method of deployment, usually there’s a couple of engineers who download and deploy Hadoop either on a single node or maybe a small cluster with four or five nodes,” Murthy explained.
What tends to happen next is a pattern that Murthy has seen many times. Staffers using the Hadoop cluster start to notice the value of the toolset. Perhaps other divisions of the company set up their own Hadoop clusters. Eventually, the value of Hadoop rises significantly and (thanks to the scalability of the underlying distributed filesystem), the separate Hadoop clusters are combined into a single large cluster with perhaps 50 or so nodes.
Original title and link: One Side of the Hadoop Adoption Story ( ©myNoSQL)