Big Data Technologies and Analytics: A Review of Emerging Solutions

Big Data Technologies and Analytics: A Review of Emerging Solutions

Hoda Ahmed Abdelhafez
Copyright: © 2014 |Pages: 17
DOI: 10.4018/ijban.2014040101
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Abstract

The internet era creates new types of large and real-time data; much of those data are non-standard such as streaming and sensor-generated data. Advanced big data technologies enable organizations to extract insights from sophisticated data. Volume, variety and velocity represent big data challenges, which cause difficulties in capture, storage, search, sharing, analysis and visualization. Therefore, technologies like No-SQL, Hadoop and cloud computing used to extract value from large volumes and a wide variety of data to discover business needs. This article's goal is to focus on the challenges of big data and how the recent technologies can be used to address those issues, which are illustrated through real world case studies. The article also presents the lessons learned from these case studies.
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Key Challenges Of Big Data

In this section, we discuss the key challenges, which are the problems with big data in a relation to traditional data. The 3Vs model, volume, variety and velocity represent big data challenges/ problems, which can be described by the following.

Volume measures the amount of data available to an organization. It can be quantified by terabytes or petabytes, and it can also be quantified by counting records, tables, transactions and files (Kaisler et al., 2013; Russom, 2011). For instance, the total book stacks in Library of Congress measures 15 Terabytes, Google processes more than 1Petabytes every hour, and Bank of America Merrill Lynch manages petabytes of data for advanced analytics & new regulatory requirements (Forsyth Communications, 2012). Traditional IT systems could not manage and analyze huge amounts of data. These data need scalable storage and distributed approach (Dumbill, 2012 a).

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