A number of significant studies are being conducted globally on pulsed plasma thrusters (PPT). However, most of these studies rely on primitive offline methods to gather and analyze data. Typically, researchers randomly select experimental data based on apparent appropriateness or accuracy for further processing. This approach leads to inefficient data usage and significant data suppression, resulting in substantial unused data and overlooked insights. Moreover, conducting PPT experiments is often costly, time-consuming, and labor-intensive, especially when repetitive execution of similar experiments is common. Consequently, these randomly selected, lossy data can lead to incorrect or inappropriate physical interpretations. There is an inherent need for a lossless, easily operable automated method to acquire and process PPT experimental data. The proposed Native (NAT) architecture is an automated and scalable Internet of Things (IoT)-based big data solution for accumulating, ingesting, and analyzing PPT data. This implementation is native to Linux operating systems and the Hadoop ecosystem, allowing for ubiquitous extension as needed. The NAT architecture provides a data warehouse for PPT behavioral data, serving as a historical footprint for future real-time analysis using machine learning and traditional analysis methods. This work can be considered a benchmark in space propulsion engineering, particularly in the PPT domain.