Pattern-Based Stream Enrichment and Aggregation Architectures for Low-Latency Financial Data Systems

Abstract

Modern financial data systems operate under stringent requirements of low latency, high throughput, fault tolerance, and correctness, driven by the real-time nature of market activity and regulatory oversight. Applications such as market data dissemination, fraud detection, real-time risk assessment, compliance monitoring, and settlement processing increasingly depend on continuous event streams rather than static datasets, as delayed or inconsistent processing can translate directly into financial loss or regulatory exposure. Within these environments, stream enrichment and aggregation form the computational core, integrating high-velocity event flows with reference datasets such as instrument metadata, customer profiles, and risk parameters, while producing higher-order insights through windowed, stateful, and temporal computations. This paper presents a pattern-based approach to stream enrichment and aggregation tailored specifically for financial systems, drawing on foundational data stream management systems (DSMS) such as Aurora and Borealis and extending their principles through modern distributed stream processing engines like Apache Flink. We systematically classify reusable enrichment and aggregation patterns, analyze their architectural and operational implications, and examine state management, fault recovery, and correctness guarantees under latency-sensitive workloads. By synthesizing early DSMS research from 2000-2005 with contemporary stream processing advancements from 2011-2018, the paper provides a structured and historically grounded framework for designing scalable, resilient, and maintainable financial streaming pipelines capable of meeting both performance and reliability demand.