User:Grmaddox/PervasiveDataRush

Pervasive DataRush is is developed by data infrastructure software company Pervasive Software, Pervasive DataRush is a data-intensive high-performance computing technology that harnesses the untapped power of multicore technology to quickly process highly reliable data sets for analytics and other business applications.

Decision Management’s James Taylor discusses Pervasive DataRush as a platform for next-generation data-intensive applications [1]. The technology behind Pervasive DataRush dates to 2004 when it began shipping as the parallel processing engine inside Pervasive Data Profiler, which went on to become a Search Data Management 2008 product of the year [2]. Pervasive DataRush was launched as a standalone offering in March 2009 [3].

In June 2009, Pervasive DataRush Chief Scientist Nena Marin, Ph.D., was selected to present a technical paper at Knowledge Discovery and Data Mining Conference in Paris. Marin discussed joint research with The University of Texas at Austin applying Pervasive DataRush to deliver highly parallel coclustering algorithms against the Netflix Prize dataset [4].

Why the technology was created
Analytics and other functions served by rapidly assembled data can bring an organization much closer to realizing strategic objectives. Growing compliance requirements, as one example, necessitate accessible data in near-real-time to make better decisions [5]. Consequently, IT personnel will be expected to be called upon to make greater in roads in providing and servicing organizational strategy objectives [5]. However, the ability of analytic applications to deliver near-real-time results is constrained by conventional technologies unable to process very large data volumes [5]. Prohibitions to this technology exist, usually cost and/or long learning curves for developers and/or users [5]. Increasingly, however, parallel programming can gain the order of magnitude speed increases related to data, including metadata [5].

Pervasive DataRush is designed to support:
Analytics

Data Mining

Electronic Discovery

Online Transaction Processing

Predictive Analytics

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