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This paper describes data management architecture for industries 4.0. The main contribution was the framework design of a platform that will support the integration from industrial Cyber-Physical Systems (iCFS), the demanding IoT environment, and communications with the cloud. The aim of such a platform will be allowing modeling of data elements, predictive functions, inference of the Key-Performance Indicators, and real-time analysis. We adopted an Attribute Driven Design (ADD) approach to architectural design that includes gathering requirements, from smart production planning, manufacturing process monitoring, and active preventive maintenance, repair and overhaul (MRO). We detail quality data management attributes and his constraints and concerns. The platform will be supporting both batch and stream data processing for the composition of data-driven events, optimization data services, embedded …