Amazon continued and Google kicked off increasingly aggressive moves into the Big Data market. Each introduced new products and services to allow enterprises to leverage Big Data analytics and storage-as-a-service with the usual benefits associated with public Cloud services (elasticity, pay-by-the-drink, trading upfront CAPEX for monthly OPEX, etc.) Specifically, Amazon introduced RedShift, an analytic-database-as-a-service, to its portfolio and struck a deal with MapR to allow customers to run its Hadoop distribution on Amazon Web Service, among other announcements. Amazon also continued to build its Elastic MapReduce business. Google finally got into the Big Data game by productizing Big Data tools and technologies, such as BigQuery, it has long used internally, and likewise introduced MapR as a service via Google Compute Engine.
Hadoop: Hadoop is an open source framework for processing, storing and analyzing massive amounts of distributed unstructured data. Originally created by Doug Cutting at Yahoo!, Hadoop was inspired by MapReduce, a user-defined function developed by Google in early 2000s for indexing the Web. It was designed to handle petabytes and exabytes of data distributed over multiple nodes in parallel.
The advent of the Web, mobile devices and other technologies has caused a fundamental change to the nature of data. Big Data has important, distinct qualities that differentiate it from �traditional� corporate data. No longer centralized, highly structured and easily manageable, now more than ever data is highly distributed, loosely structured (if structured at all), and increasingly large in volume.
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