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At times, Big Data gets so large and complex, that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, curation, storage, search, sharing, transfer, analysis and visualization. Manufacturers have invested heavily in their data infrastructure, pouring millions into their Product Lifecycle Management (PLM), Enterprise Resource Planning (ERP), Computer-Aided Design (CAD) and other critical supply chain systems. For manufacturers, difficulty lies in managing all that data, which is continually being pumped into these environments.

