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It provides economies of storage and processing that are beyond the reach of the traditional RDBMS, and it offers an ability for the storage and processing of unstructured and semi-structured data for which the RDBMS had no real solution. More than any other technology, Hadoop has rightfully been associated with the Big Data movement. However, while Hadoop provides a framework for the mass processing of data, it doesn’t have a framework for transactional and online operations. As we will see in the next chapter, bleeding-edge websites demanded not just a storage and batch processing solution but also an online transactional solution.

The degree to which Hadoop has become the de facto solution for massive unstructured data storage and processing can be illustrated by the positions taken by the top three database vendors: Microsoft, Oracle, and IBM. By 2012, each of these giants had ceased to offer any form of Hadoop alternative and instead were offering Hadoop within its product portfolio. The Power of Hadoop Hadoop provides an economically attractive storage solution for Big Data, as well as a scalable processing model for analytic processing.

However, it took only a few years for Google to shift to masses of rack-mounted servers in commercial-grade data centers. As Google grew, it outstripped 23 Chapter 2 ■ Google, Big Data, and Hadoop the capacity limits of even the most massive existing data center architectures. The economics and practicalities of delivering a hardware infrastructure capable of unbounded exponential growth required Google to create a new hardware and software architecture. Google committed to a number of key tenants when designing its data center architecture.

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