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The Evolution of the Massively Parallel Processing Database in Support of Visual Analytics

The Evolution of the Massively Parallel Processing Database in Support of Visual Analytics

Ian A. Willson
Copyright: © 2011 |Volume: 24 |Issue: 4 |Pages: 26
ISSN: 1040-1628|EISSN: 1533-7979|EISBN13: 9781613505267|DOI: 10.4018/irmj.2011100101
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MLA

Willson, Ian A. "The Evolution of the Massively Parallel Processing Database in Support of Visual Analytics." IRMJ vol.24, no.4 2011: pp.1-26. http://doi.org/10.4018/irmj.2011100101

APA

Willson, I. A. (2011). The Evolution of the Massively Parallel Processing Database in Support of Visual Analytics. Information Resources Management Journal (IRMJ), 24(4), 1-26. http://doi.org/10.4018/irmj.2011100101

Chicago

Willson, Ian A. "The Evolution of the Massively Parallel Processing Database in Support of Visual Analytics," Information Resources Management Journal (IRMJ) 24, no.4: 1-26. http://doi.org/10.4018/irmj.2011100101

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Abstract

This article explores the evolution of the Massively Parallel Processing (MPP) database, focusing on trends of particular relevance to analytics. The dramatic shift of database vendors and leading companies to utilize MPP databases and deploy an Enterprise Data Warehouse (EDW) is presented. The inherent benefits of fresher data, storage efficiency, and most importantly accessibility to analytics are explored. Published industry and vendor metrics are examined that demonstrate substantial and growing cost efficiencies from utilizing MPP databases. The author concludes by reviewing trends toward parallelizing decision support workload into the database, ranging from within database transformations to new statistical and spatial analytic capabilities provided by parallelizing these algorithms to execute directly within the MPP database. These new capabilities present an opportunity for timely and powerful enterprise analytics, providing a substantial competitive advantage to those companies able to leverage this technology to turn data into actionable information, gain valuable new insights, and automate operational decision making.

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