Scalable Stream Processing Frameworks for Industry 4.0 Applications

Abstract

: This paper examines the design, evaluation, and applicability of scalable stream processing frameworks for Industry 4.0 environments, where real-time, high-throughput data flows originate from interconnected industrial sensors, IoT devices, and cyber-physical systems. We spotlight Pathway, a unified streaming and batch processing framework engineered in Rust for both bounded and unbounded data use cases, targeting physical-economy workloads typical in Industry 4.0 contexts arXiv. Additionally, we analyze a comprehensive benchmarking study of stream processing frameworks—including Apache Flink, Kafka Streams, Samza, Hazelcast Jet, and Apache Beam—deployed as microservices in cloud environments, evaluating their scalability across Kubernetes clusters under heavy loads arXivScienceDirect. Our methodology synthesizes these findings to assess how these frameworks can meet the rigorous demands of industrial automation, including low-latency analytics, fault tolerance, and dynamic scaling. Based on literature, we propose a tailored benchmarking scenario for Industry 4.0—emphasizing sensor-driven high-frequency data, complex event processing (CEP), and iterative analytics—building on proven microservice benchmarking methodologies SpringerLinkFrontiers. Our results section interprets expected trade-offs: frameworks like Flink exhibit strong low-latency performance and scalability SpringerLinkMDPI, while unified engines like Pathway demonstrate superior throughput in complex workloads arXivPathway. We further discuss limitations and practical considerations such as resource demands, edge deployment feasibility, and the adaptability to industrial protocols. We conclude by recommending hybrid architectures combining low-latency engines with unified frameworks for machine learning tasks, and propose future work toward real-world deployments with live IoT streams and evolving industrial requirements.