Engineering at Scale: Fanout, De-duplication, and Latency in Real-Time Event Platforms

Abstract

Modern real-time event platforms serve as critical infrastructure for digital ecosystems, delivering time-sensitive information across diverse applications from financial services to conversational interfaces. These platforms confront escalating demands while navigating the inherent tension between speed, reliability, and accuracy. This article explores architectural foundations enabling massive-scale message distribution, including publish-subscribe models, partitioned event buses, and geographic distribution strategies. It delves into de-duplication techniques essential for preventing message duplication across complex topologies, from deterministic fingerprinting to machine learning-based approaches. The discussion extends to multi-surface orchestration mechanisms that intelligently route notifications across heterogeneous device ecosystems based on contextual factors and recipient state. Performance optimization methodologies, including latency budgeting, predictive resource allocation, and graceful degradation patterns, complete this examination of scalable event platform design. Throughout, particular attention focuses on how artificial intelligence transforms traditional event processing into dynamic, context-aware systems capable of autonomous operation at a global scale.