Abstract

Real-time data streaming is a transformative technology for fuel stations, enabling enhanced monitoring, fraud detection, and operational efficiency. This paper explores the architecture, implementation, and optimization of a robust software system using AWS Kinesis for real-time streaming of fuel dispenser and Automatic Tank Gauge (ATG) data. Multiple Kinesis streams are used to distribute data effectively, with streams dedicated to dispenser transactions, ATG data, and ATG alerts. The system ensures scalability, low latency, and data redundancy by leveraging AWS S3 for backup. Through detailed analysis and a pilot implementation, this paper highlights the significant improvements in efficiency and data reliability achieved through this approach, along with recommendations for future enhancements.