An Iterative Methodology for Defining Big Data Analytics Architectures

Thanks to the advances achieved in the last decade, the lack of adequate technologies to deal with Big Data characteristics such as Data Volume is no longer an issue. Instead, recent studies highlight that one of the main Big Data issues is the lack of expertise to select adequate technologies and build the correct Big Data architecture for the problem at hand. In order to tackle this problem, we present our methodology for the generation of Big Data pipelines based on several requirements derived from Big Data features that are critical for the selection of the most appropriate tools and techniques. Thus, thanks to our approach we reduce the required know-how to select and build Big Data architectures by providing a step-by-step methodology that leads Big Data architects into creating their Big Data Pipelines for the case at hand. Our methodology has been tested in two use cases.This work has been funded by the ECLIPSE project (RTI2018-094283-B-C32) from the Spanish Ministry of Science, Innovation and Universities