Designing Scalable Architectures for Real-Time Big Data Stream Processing in the Cloud

Abstract

We explores the design of scalable architectures for real-time big data stream processing in the cloud. Real-time data analytics faces unique challenges due to the high-velocity and high-volume nature of incoming streams. The core objectives here are to ensure low-latency processing, fault tolerance, and elasticity for handling fluctuating workloads. We propose an approach that employs dynamic allocation of computing resources to balance throughput and processing latency, while respecting cost constraints in multi-tenant cloud environments. This approach builds on fundamental queueing and probabilistic models to capture uncertainties and bursty traffic patterns and integrates advanced load-balancing and parallelization techniques to accommodate large-scale clusters. Furthermore, we emphasize the interplay between horizontal and vertical scaling decisions to optimize resource utilization. We develop a mathematical framework that addresses how data ingestion rates, system reliability, and fault recovery requirements inform the necessary parallelism level and distributed storage layout. Experimental findings suggest that a carefully tuned architecture can reduce end-to-end latency while maintaining operational cost within practical limits. We then examine limitations, including the potential for bottlenecks within specific nodes or network links, along with a discussion of how dynamic workload profiles can strain resource allocation strategies. We conclude by articulating future directions for refining the proposed approach to meet ever-evolving requirements.