Integration of IoT Sensors with Databricks for Electrical Equipment Monitoring

Abstract

This manuscript explores the integration of Internet of Things (IoT) sensors with Databricks to enhance the monitoring of electrical equipment, focusing specifically on low-latency data handling crucial for fast decision-making in financial systems. As industries increasingly adopt IoT technologies, the challenge of managing large volumes of data generated by sensors in real time has become paramount. Traditional monitoring methods often struggle with the speed and efficiency required in today’s fast-paced environments, leading to delays in decision-making and potential operational inefficiencies.

In this study, we outline the architecture that combines IoT sensors and Databricks, a cloud-based platform for big data analytics. This integration allows for seamless data ingestion, processing, and visualization, thereby facilitating real-time insights into electrical equipment performance. The research methodology involved setting up a testbed that simulates real-world operational conditions, wherein data was collected from various types of sensors installed on electrical equipment. These sensors continuously monitored parameters such as voltage, current, and temperature, generating data at high frequencies.

The results demonstrate significant improvements in processing speed and decision-making capabilities, showcasing a reduction in latency from several seconds to milliseconds when utilizing the Databricks platform. This rapid processing translates to better resource utilization, reduced downtime, and enhanced operational efficiency. By leveraging the combined power of IoT sensors and cloud analytics, organizations can gain timely insights, ultimately driving smarter financial decisions. This research highlights the transformative potential of integrating IoT with advanced data analytics platforms and provides a framework for future implementations in various industrial contexts.