2 HPCC Systems: Data Intensive Supercomputing Solutions Executive Summary

As a result of the continuing information explosion, many organizations are drowning in data and the resulting “data gap ” or inability to process this information and use it effectively is increasing at an alarming rate. Data-intensive computing represents a new computing paradigm which can address the data gap using scalable parallel processing and allow government and commercial organizations and research environments to process massive amounts of data and implement applications previously thought to be impractical or infeasible. The fundamental challenges of data-intensive computing are managing and processing exponentially growing data volumes, significantly reducing associated data analysis cycles to support practical, timely applications, and developing new algorithms which can scale to search and process massive amounts of data. LexisNexis believes that the answer to these challenges is a scalable, integrated computer systems hardware and software architecture designed for parallel processing of data-intensive computing applications. This paper explores the challenges of data-intensive computing and offers an in-depth comparison of commercially available system architectures including the LexisNexis Data Analytics Supercomputer (DAS) also referred to as the LexisNexis High-Performance Computing Cluster (HPCC), and Hadoop, an open source implementation of Google’s MapReduce architecture. The MapReduce architecture and programming model pioneered by Google is an example of a systems architecture specifically designed for processing and analyzing large datasets. This architecture was designed to run on larg