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What is Data-Intensive Computing

Chapter 2

Handbook of Research on Big Data Storage and Visualization Techniques

Computing applications which require large volumes of data and devote most of their processing time to I/O and manipulation of data are deemed data-intensive. (Data-intensive computing, 2017)

Published in Chapter:

Overview of Big-Data-Intensive Storage and Its Technologies

Richard S. Segall (Arkansas State University, USA) and Jeffrey S. Cook (Independent Researcher, USA)

Source Title: Handbook of Research on Big Data Storage and Visualization Techniques

DOI: 10.4018/978-1-5225-3142-5.ch002

Abstract

This chapter deals with a detailed discussion on the storage systems for data-intensive computing using Big Data. The chapter begins with a brief introduction about data-intensive computing and types of parallel processing approaches. It also highlights the points that display how data-intensive computing systems differ from other forms of computing. A discussion on the importance of Big Data computing is put forth. The current and future challenges of storage in genomics are discussed in detail. Also, storage and data management strategies are given. The chapter's focus is then on the software challenges for storage. Storage use cases are provided like DataDirect Networks, SDSC, etc. The list of storage tools and their details are provided. A small section discusses the sensor data storage system. Then a table is provided that shows the top 10 cloud storage systems for data-intensive computing using Big Data in the world. Top 500 Big Data storage servers statistics are also displayed effectively by the images from Top500 website.

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