Industries are generally data rich but information poor environments. Massive datagenerated in industrial operations is traditionally neglected (or simply took aside) mainly due to systems design restrictions, to the lack of adequate processing power of typically installedcomputing infrastructure and to a sector culture notably focused on collecting, selecting,storing and preserving historical series in on-demand access repositories. This huge amountof unprocessed data resting in these repositories is a latent source of information that could beused to improve industrial processes. This work then proposes an approach in which a propercomputing power processing engine is plugged-in to current industrial information infrastructureto provide it with the ability of handling massive industrial data. Testing on real-world industrialdata volumes of 5GB, 50GB and 100GB attested the effectiveness and potential of the proposedapproach in dealing with Industrial Big Data.