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Building the Unstructured Data Warehouse: Architecture, Analysis, and Design by Krish Krishnan, W. H. Inmon

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Whenever large volumes of data are encountered, it is always an advantage to be able to process the workload in parallel. By processing the workload in parallel, the total elapsed time for processing is reduced. If n units of work need to be done and there are m processors, then the elapsed time required to do the processing is approximately n/m. (The n/m factor depends on being able to divide the workload evenly among the processors.)

Given that the results of Textual ETL processing are compatible, it is then easy to divide the input that needs to be processed into roughly separate and equal workloads and to use separate processors to operate on the workload. Of course, a parallel architecture can be used for this ...

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