The proliferation of low-cost sensors and industrial data solutions have continued to push the frontier of manufacturing technology.
Machine Learning and other advanced statistical techniques stand to provide tremendous advantages in production capabilities, optimization, monitoring, and efficiency.
The tremendous volume of data gathered continues to grow, and the methods for storing the data are critical underpinnings for advancing manufacturing technology.
This work aims to investigate the ramifications and design trade offs of two prominent Database Management Systems: SQL and NoSQL.
The technologies and accompanying design constraints are investigated, and a side-by-side comparison is carried out through high-fidelity industrial data simulated load tests using metrics from a major US manufacturer.