2025/08/20 by Y. Yang, Yang, Yunzhao, Runhui Wang +28
Computer Science · Decision Sciences · #Cloud Computing and Resource Management #Distributed #FOS: Computer and information sciences #Parallel #Scientific Computing and Data Management #Software System Performance and Reliability #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2508.15105
openalex publication_date 2025/08/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Modern distributed data processing systems struggle to balance performance, maintainability, and developer productivity when integrating machine learning at scale. These challenges intensify in large collaborative environments due to high communication overhead and coordination complexity. We present a "Declarative Data Pipeline" (DDP) architecture that addresses these challenges while processing billions of records efficiently. Our modular framework seamlessly integrates machine learning within Apache Spark using logical computation units called Pipes, departing from traditional microservice approaches. By establishing clear component boundaries and standardized interfaces, we achieve modularity and optimization without sacrificing maintainability. Enterprise case studies demonstrate substantial improvements: 50% better development efficiency, collaboration efforts compressed from weeks to days, 500x scalability improvement, and 10x throughput gains.