Current trends in industrial systems opt for the use of different big-data engines as a means to process huge amounts of data that cannot be processed with an ordinary infrastructure. The number of issues an industrial infrastructure has to face is large and includes challenges such as the definition of different efficient architecture setups for different applications, and the definition of specific models for industrial analytics. In this context, this paper explores the development of a medium size big-data engine (i.e., implementation) able to improve the performance in map-reduce computing by splitting the analytic into different segments that may be processed by the engine in parallel using a hierarchical model. This type of facility reduces end-to-end computation time for all segments with their results then merged with other information from other segments after their processing in parallel. This type of setup increases the performance of current clusters improving I/O operations remarkably as empirical results revealed.
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