  Smart city management is going through a remarkable transition, in terms of
quality and diversity of services provided to the end-users. The stakeholders
that deliver pervasive applications are now able to address fundamental
challenges in the big data value chain, from data acquisition, data analysis
and processing, data storage and curation, and data visualisation in real
scenarios. Industry 4.0 is pushing this trend forward, demanding for
servitization of products and data, also for the smart cities sector where
humans, sensors and devices are operating in strict collaboration. The data
produced by the ubiquitous devices must be processed quickly to allow the
implementation of reactive services such as situational awareness, video
surveillance and geo-localization, while always ensuring the safety and privacy
of involved citizens. This paper proposes a modular architecture to (i)
leverage innovative technologies for data acquisition, management and
distribution (such as Apache Kafka and Apache NiFi), (ii) develop a multi-layer
engineering solution for revealing valuable and hidden societal knowledge in
smart cities environment, and (iii) tackle the main issues in tasks involving
complex data flows and provide general guidelines to solve them. We derived
some guidelines from an experimental setting performed together with leading
industrial technical departments to accomplish an efficient system for
monitoring and servitization of smart city assets, with a scalable platform
that confirms its usefulness in numerous smart city use cases with different
needs.
