ADVANCEMENTS IN REAL-TIME STREAM PROCESSING: A COMPARATIVE STUDY OF APACHE FLINK, SPARK STREAMING, AND KAFKA STREAMS

Abstract

This article presents a comprehensive comparative analysis of three leading stream processing platforms: Apache Flink, Spark Streaming, and Kafka Streams, examining their architectural approaches, performance characteristics, and operational considerations in real-time data processing scenarios. Through extensive benchmarking and evaluation, we investigated these platforms across multiple dimensions, including processing latency, throughput capacity, resource utilization, and operational complexity. The article reveals that Apache Flink demonstrates superior performance in low-latency scenarios with its true streaming model, while Spark Streaming excels in high-throughput situations with its micro-batch approach and robust ecosystem integration. Kafka Streams emerge as a compelling solution for lightweight stream processing needs, particularly in Kafka-centric architectures. The article also uncovers a significant convergence of features across these platforms, with each adopting strengths from the others while maintaining their distinct architectural advantages. Performance benchmarks indicate that Flink consistently achieves sub-100-millisecond latency for complex operations, Spark Streaming offers unparalleled throughput for large-scale data processing, and Kafka Streams provides the most straightforward operational model. These insights, combined with detailed use case analyses, provide organizations with crucial decision-making criteria for selecting the most appropriate stream processing platform based on their specific requirements, existing infrastructure, and technical expertise.