Convergent Data Systems: The Intersection Of Real-Time Feature Stores, Multi-Table ACID Transactions, And Fine-Grained Stream Processing

Abstract

This article explores the convergence of three critical data system paradigms: real-time feature stores, multi-table transactional data lakes, and adaptive stream processing. It examines how feature stores address the training-serving skew problem while enabling ultra-low latency ML serving capabilities. The article shows innovations in providing cross-table ACID guarantees for data lakes, overcoming limitations in existing table formats while maintaining compatibility with established ecosystems. The article further shows advances in stream processing resource management through systems, which optimize CPU/memory allocation and operator parallelism. Finally, it evaluates integration opportunities across these previously siloed technologies, identifying common architectural patterns, addressing research gaps, and exploring practical enterprise adoption considerations. The findings suggest significant operational and economic benefits from unified data architectures that preserve specialized capabilities while reducing complexity, ultimately enabling more efficient and effective data pipelines for modern analytics and machine learning workloads.