  Billions of interconnected Internet of Things (IoT) sensors and devices
collect tremendous amounts of data from real-world scenarios. Big data is
generating increasing interest in a wide range of industries. Once data is
analyzed through compute-intensive Machine Learning (ML) methods, it can derive
critical business value for organizations. Powerfulplatforms are essential to
handle and process such massive collections of information cost-effectively and
conveniently. This work introduces a distributed and scalable platform
architecture that can be deployed for efficient real-world big data collection
and analytics. The proposed system was tested with a case study for Predictive
Maintenance of Home Appliances, where current and vibration sensors with high
acquisition frequency were connected to washing machines and refrigerators. The
introduced platform was used to collect, store, and analyze the data. The
experimental results demonstrated that the presented system could be
advantageous for tackling real-world IoT scenarios in a cost-effective and
local approach.
