SCALABLE EVENT-DRIVEN ARCHITECTURESFOR REAL-TIME DATA PROCESSING: AFRAMEWORK FOR DISTRIBUTED SYSTEMS
Keywords:
Real-time, Data Processing, Distributed Systems Architecture, Stream Processing Frameworks, Event-Driven Design, Scalable MicroservicesAbstract
A significant challenge in modern data-driven organizations is the development of scalable architectures capable of processing real-time data streams efficiently. This article presents a comprehensive framework for building and optimizing real-time data processing systems, focusing on integrating contemporary technologies such as Apache Kafka, Apache Flink, and Spark Streaming. The article systematically analyzes microservices architectures and event-driven design patterns, and it examines various approaches to data ingestion, state management, and dynamic load balancing in distributed environments. The methodology combines an extensive literature review with multiple case studies across finance, e-commerce, and IoT sectors, supplemented by prototype implementations to validate the proposed architectural patterns. Performance testing under various load conditions reveals significant system responsiveness and throughput improvements when implementing our recommended architectural patterns. The article contributes to the field by providing actionable strategies for organizations seeking to build scalable real-time processing systems, offering insights into optimal infrastructure configuration and maintenance. The findings demonstrate that careful consideration of architectural choices and appropriate technology selection can enable organizations to effectively manage real-time data streams while maintaining system resilience and performance.
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