Big Data solutions on a small scale: Evaluating accessible high-performance computing for social research

Murthy, Dhiraj and Bowman, S. A.. 2014. Big Data solutions on a small scale: Evaluating accessible high-performance computing for social research. Big Data & Society, 1(2), ISSN 2053-9517 [Article]

Big Data & Society-2014-Murthy-.pdf - Published Version
Available under License Creative Commons Attribution Non-commercial.

Download (339kB) | Preview

Abstract or Description

Though full of promise, Big Data research success is often contingent on access to the newest, most advanced, and often expensive hardware systems and the expertise needed to build and implement such systems. As a result, the accessibility of the growing number of Big Data-capable technology solutions has often been the preserve of business analytics. Pay as you store/process services like Amazon Web Services have opened up possibilities for smaller scale Big Data projects. There is high demand for this type of research in the digital humanities and digital sociology, for example. However, scholars are increasingly finding themselves at a disadvantage as available data sets of interest continue to grow in size and
complexity. Without a large amount of funding or the ability to form interdisciplinary partnerships, only a select few find themselves in the position to successfully engage Big Data. This article identifies several notable and popular Big Data technologies typically implemented using large and extremely powerful cloud-based systems and investigates the feasibility and utility of development of Big Data analytics systems implemented using low-cost commodity hardware in basic and easily maintainable configurations for use within academic social research. Through our investigation and experimental
case study (in the growing field of social Twitter analytics), we found that not only are solutions like Cloudera’s
Hadoop feasible, but that they can also enable robust, deep, and fruitful research outcomes in a variety of use-case scenarios across the disciplines.

Item Type:


Identification Number (DOI):


Big Data, social media research methods, Big Data research methods, digital humanities, digital sociology, Twitter

Related URLs:

Departments, Centres and Research Units:



25 November 2014Published

Item ID:


Date Deposited:

04 Dec 2014 18:33

Last Modified:

15 Apr 2021 15:09

Peer Reviewed:

Yes, this version has been peer-reviewed.


View statistics for this item...

Edit Record Edit Record (login required)