Aerospike is a low-latency NoSQL database, and an important application area is in high-frequency trading (HFT) and fraud detection in the financial services field. The paper will examine how Aerospike’s distributed architecture, in-memory storage, and a key-value data model make it fast, scalable, and reliable to process massive amounts of data in real-time. The latency between trade execution and order book management is kept to a minimum in HFT, where milliseconds mean the difference between profitability and loss, with Aerospike. It also enables real-time processing of the market data, which authorizes financial institutions to develop quick decisions that could maximize trading strategies. With machine learning being used in identifying fraud, Aerospike uses machine learning to read highly transactional data and detect fraudulent actions before they ever take place. Another prominent feature of Aerospike described in the paper is its scalability, which can provide data volumes that grow over time without compromising performance, and its compatibility with other financial technologies, including machine learning models and predictive analytics. This paper shows the comparison of the results of the experiments carried out in the study, where Aerospike proved its excellent performance relative to traditional relational databases, such as MySQL, and other databases like MongoDB and Cassandra, as NoSQL databases. These results show that Aerospike is far superior in terms of latency and throughput to these systems. In conclusion, Aerospike is a software that can prove helpful to financial institutions interested in strengthening real-time decision-making and fraud prevention capacities. Its fusion with other technologically advanced technologies, such as artificial intelligence, blockchain, and so on, could be researched in the future, also to enhance its functionalities as the world of financial services is changing rapidly.

High-Frequency Trading (HFT), Fraud Detection, Low-Latency Databases, Aerospike, Real-Time Data Processing

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