Scalable Event-Driven Architectures for Real-Time Big Data Applications

Abstract

Scalable Event-Driven Architectures (EDAs) provide an efficient approach for real-time big data processing by enabling low-latency, asynchronous, and continuous handling of high-speed data streams generated from IoT devices, cloud platforms, social media, and enterprise systems. This study presents a scalable framework that integrates distributed event brokers, stream processing engines, cloud-native microservices, and scalable storage solutions. The architecture utilizes publish-subscribe communication, event sourcing, and distributed stream analytics to support real-time decision-making and dynamic scalability. Technologies such as Apache Kafka, Apache Flink, Apache Spark Streaming, and Kubernetes enhance throughput, fault tolerance, and system resilience. Key components include event producers, brokers, stream processors, consumers, and monitoring services. Performance evaluation based on throughput, latency, scalability, fault tolerance, and resource utilization demonstrates that EDAs outperform traditional batch-processing and request-response systems. The framework also addresses challenges such as event ordering, state management, event replay, and observability through checkpointing, event partitioning, distributed tracing, and container orchestration. The proposed architecture is applicable to financial analytics, smart cities, healthcare, e-commerce, cybersecurity, and industrial automation. Overall, EDAs offer a scalable, reliable, and flexible foundation for next-generation real-time analytics and big data applications.