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The well-known 3V architectural paradigm for Big Data introduced by Laney(2011), provides a simplified framework for defining the architecture of a bigdata platform to be deployed in various scenarios tackling processing ofmassive datasets. While additional components such as Variability and Veracityhave been discussed as an extension to the 3V model, the basic components(volume, variety, velocity) provide a quantitative framework while variabilityand veracity target a more qualitative approach. In this paper we argue why thebasic 3V's are not equal due to the different requirements that need to becovered in case higher demands for a particular "V". Similar to otherconjectures such as the CAP theorem 3V based architectures differ on theirimplementation. We call this paradigm heterogeneity and we provide a taxonomyof the existing tools (as of 2013) covering the Hadoop ecosystem from theperspective of heterogeneity. This paper contributes on the understanding ofthe Hadoop ecosystem from the perspective of different workloads and aims tohelp researchers and practitioners on the design of scalable platformstargeting different operational needs.

On the inequality of the 3V's of Big Data Architectural Paradigms: A case for heterogeneity