Page 141 - Data Architecture

Chapter 4.3: Parallel Processing

Fig. 4.3.3 Processors executing independently.

An interesting thing about parallelization is that the total number of machine cycles
required to process big data is not reduced by parallelization. In fact, the total number of
machine cycles required is actually raised by parallelization, due to the fact that
coordination of processing across different nodes is now required. Instead, the total
elapsed time is what is reduced by introducing parallelization. The more parallelization

there is, the less elapsed time there is to manage the data found in big data.

There are different forms of parallelization. The Roman census method is not the only

form of parallelization. Another classical form of parallelization is that seen in Fig. 4.3.4.

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