EVENT-DRIVEN API-BASED ARCHITECTURE SUPPORTING REAL-TIME DISTRIBUTED DATABASES

Abstract

Event-driven architectures have become highly important in the context of enabling the processing of data in real time with respect to large-scale distributed systems. In this research, we are analyzing API architecture design and implementation based on event-driven architecture, which is optimized to work with a real-time distributed database, with special attention to the Apache Cassandra integration. Traditional pull models of API communication will often fail to meet the scalability and reactiveness requirements of a fast-speed data paradigm; however, event-driven APIs can be used to support asynchronous communication, therefore, reducing system bottlenecks and promoting service resiliency. The study uses a conceptual and analytical approach to evaluate the interaction of APIs on events with distributed data storage to maintain the continuous processes of data ingestion and processing. An artificial event-driven model is painstakingly explored to research the event lifetime consistency, time behavior, and data distribution patterns in Cassandra. The results indicate that event-driven API architectures significantly enhance scalability as well as responsiveness in real-time, and Cassandra is used successfully as a foundation of high-throughput, distributed data storage.