  The well-known 3V architectural paradigm for Big Data introduced by Laney
(2011), provides a simplified framework for defining the architecture of a big
data platform to be deployed in various scenarios tackling processing of
massive datasets. While additional components such as Variability and Veracity
have been discussed as an extension to the 3V model, the basic components
(volume, variety, velocity) provide a quantitative framework while variability
and veracity target a more qualitative approach. In this paper we argue why the
basic 3V's are not equal due to the different requirements that need to be
covered in case higher demands for a particular "V". Similar to other
conjectures such as the CAP theorem 3V based architectures differ on their
implementation. We call this paradigm heterogeneity and we provide a taxonomy
of the existing tools (as of 2013) covering the Hadoop ecosystem from the
perspective of heterogeneity. This paper contributes on the understanding of
the Hadoop ecosystem from the perspective of different workloads and aims to
help researchers and practitioners on the design of scalable platforms
targeting different operational needs.
