Designing an Archival System for Long-Term Fuel System Data Analysis

The increasing volume of fuel transaction data necessitates efficient long-term archival solutions to enable historical analysis, regulatory compliance, and anomaly detection. Traditional storage methods may become inefficient due to high storage costs and data retrieval latencies, especially as fuel stations generate continuous data streams. This paper proposes an archival system designed to store and retrieve long-term fuel transaction data efficiently, ensuring optimized performance without unnecessary overhead. Leveraging tiered storage models, metadata indexing, and cloud- based solutions such as MongoDB’s native archiving capabilities, this system transitions data older than three months to an archival repository where it remains accessible on demand. MongoDB’s TTL indexes and sharding features provide efficient management of archived records, enabling retrieval without performance degradation. The system also employs metadata-based indexing and hierarchical storage techniques, improving query efficiency and reducing latency in accessing historical data. The archival system ensures data integrity, scalability, and compliance with industry regulations, offering an adaptable solution for long-term storage needs in fuel transaction management. A performance evaluation of various storage solutions highlights the benefits of using hierarchical storage, optimized query mechanisms, and cost-effective data retention strategies to enhance long-term data accessibility and regulatory compliance. Keywords: Fuel transaction archiving, long-term data storage, hierarchical storage, cloud-based archival, regulatory compliance, data retrieval optimization, MongoDB archiving.