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Research Report - End-User
Insights Across the Hybrid Enterprise: Big Data 2015
Date: 04/07/2015 Length: 71 pages Cost: $795.00

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Abstract:
Since its inception, the concept of "big data" has been widely associated with a single data management platform--Hadoop. This connection may be due to the popularity of the use of the Hadoop platform to store and process large amounts of multi-structured data. However, Hadoop is not the only answer to the question of "what is big data?"

As was explored in the inaugural EMA/9sight survey in 2012, big data is both a way to look at new sources of data and how organizations place that information under management. Big data has attracted a wide range of application innovators as well as many protesters against the dominance of relational databases and data warehouses. The EMA/9sight surveys use a deliberately broad definition of big data to inspire end users to think beyond the box of Hadoop. The survey explores the wide range of ways in which non-traditional data, often in combination with more traditional types, has enabled new or improved business processes. As was established in previous studies in 2012 and 2013 and again in 2014, big data offers a wide range of possibilities, but the name "big data" itself keeps media and industry eyes focused on size as the defining feature.

With the 2014 survey, these previous observations continue to hold true. However, there is an evolving market in which size is not everything, and speed, in all its aspects, has grown in importance for respondents. Furthermore, respondents continue to include a wide range of data structures, from highly irregular to strongly modeled, within the scope of their projects. This refocusing of implementers’ attention on speed and structure reduces overall growth in big data by some measures and requires EMA/9sight to explore how consideration of speed and structure are changing market dynamics. The 2014 survey also included investigations into the highly visible topics of data-driven cultures, the Internet of Things, and data lake architectures.
Author:

John Myers, Former EMA Analyst

Other Contributor:

Dr. Barry Devlin

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