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Parallel data analysis is a method for analyzing data using parallel processes that run simultaneously on multiple computers.
The process is used in the analysis of large data sets such as large telephone call records, network logs and web repositories for text documents which can be too large to be placed in a single relational database. The method is often used in Big Data Analytics and general data analysis.
The primary concept behind parallel data analysis is parallelism, defined in computing as the simultaneous execution of processes.
This is often achieved by using multiple processors or even multiple computers and is a common practice in distributed computing. In the parallel analysis of data, different computers performing different aspects of data analysis simultaneously execute these processes and then later consolidate the results into a single large report.
The reason for this parallelism is mainly to make analysis faster, but it is also because some data sets may be too dynamic, too large or simply too unwieldy to be placed efficiently in a single relational database. The result would be that those data sets are housed in different databases optimized for that kind of data and in different machines, so linear analysis simply won’t be an efficient option.