Cloud based evaluation of databases for stock market data

Area of Science:

• Computer Science
• Financial Technology
• Database Management

Background:

• Electronic markets and automated trading have transformed financial data analysis.
• Open High Low Close prices and Volume data are crucial for market insights and forecasting.
• Traditional relational databases face challenges with increasing data volumes and hybrid cloud adoption.

Purpose of the Study:

• To evaluate the performance of various databases in a hybrid cloud environment.
• To determine the most suitable database for storing and analyzing financial time-series data.
• To assess database suitability considering cost, scalability, and fault tolerance.

Main Methods:

• Defined a comprehensive set of criteria for database performance evaluation.
• Conducted experiments using standard and custom workloads on a hybrid cloud.
• Analyzed performance metrics including cost, storage, and throughput.

Main Results:

• MongoDB demonstrated superior performance in terms of cost, storage efficiency, and throughput compared to other databases.
• Cloud-native solutions were identified as optimal for leveraging autoscaling and cloud maintenance capabilities.
• The study provides empirical evidence for database selection in financial data management.

Conclusions:

• Migration to MongoDB is recommended for financial institutions seeking improved performance and cost-efficiency.
• Adopting cloud-native database solutions is crucial for maximizing the benefits of hybrid cloud environments.
• The findings support a shift from traditional relational databases to more scalable and efficient alternatives for financial data.