An Introduction to Big Data, High Performance Computing, High Throughput Computing, and Hadoop -- Using High Performance Computing for Conquering Big Data -- Data Movement in Data-Intensive High Performance Computing -- Using Managed High Performance Computing Systems for High Throughput Computing -- Accelerating Big Data Processing on Modern HPC Clusters -- dispel4py: An Agile Framework for Data-Intensive Methods Using HPC -- Big Data Performance Analysis Tool for HPC Applications and Scientific Clusters -- Big Data behind Big Data -- Empowering R with High Performance Computing Resources for Big Data Analytics -- Big Data Techniques as a Solution to Theory Problems -- High-Frequency Financial Statistics through High Performance Computing -- Large-scale Multi-Modal Data Exploration with Human in the Loop -- Using High Performance Computing for Detecting Duplicate, Similar and Related Images in a Large Data Collection -- Big Data Processing in the eDiscovery Domain -- Databases and High Performance Computing -- Conquering Big Data Through the Support of the Wrangler Supercomputer.

This book provides an overview of the resources and research projects that are bringing Big Data and High Performance Computing (HPC) on converging tracks. It demystifies Big Data and HPC for the reader by covering the primary resources, middleware, applications, and tools that enable the usage of HPC platforms for Big Data management and processing. Through interesting use-cases from traditional and non-traditional HPC domains, the book highlights the most critical challenges related to Big Data processing and management, and shows ways to mitigate them using HPC resources. Unlike most books on Big Data, it covers a variety of alternatives to Hadoop, and explains the differences between HPC platforms and Hadoop. Written by professionals and researchers in a range of departments and fields, this book is designed for anyone studying Big Data and its future directions. Those studying HPC will also find the content valuable.