The anatomy of big data computing

TLDR

Advances in information technology across business, engineering, medical, and scientific fields have led to an information/data explosion, making knowledge discovery and decision‑making from rapidly growing voluminous data a challenging task that has spurred the emergence of big data computing. This paper examines the evolution of big data computing, contrasts it with traditional data warehousing, presents a taxonomy and underlying technologies, outlines the integrated big data‑cloud platform and layered architecture, and identifies open technical challenges and future directions. Big data computing requires massive storage and computing resources for data curation and processing, which can be provisioned from on‑premise or cloud infrastructures. © 2015 John Wiley & Sons, Ltd.

Abstract

Summary Advances in information technology and its widespread growth in several areas of business, engineering, medical, and scientific studies are resulting in information/data explosion. Knowledge discovery and decision‐making from such rapidly growing voluminous data are a challenging task in terms of data organization and processing, which is an emerging trend known as big data computing , a new paradigm that combines large‐scale compute, new data‐intensive techniques, and mathematical models to build data analytics. Big data computing demands a huge storage and computing for data curation and processing that could be delivered from on‐premise or clouds infrastructures. This paper discusses the evolution of big data computing, differences between traditional data warehousing and big data, taxonomy of big data computing and underpinning technologies, integrated platform of big data and clouds known as big data clouds, layered architecture and components of big data cloud, and finally open‐technical challenges and future directions. Copyright © 2015 John Wiley & Sons, Ltd.