Architecting Event-driven Stream Processing: Technologies for Reliability and Analytical Availability in Distributed Computing Systems

This article explores architectural and technological approaches to building stream processing systems focused on high reliability and analytical availability. It highlights event ordering mechanisms, event-time processing, state management, and fault tolerance using Apache Kafka and Apache Flink. The study analyzes state recovery strategies, latency optimization, and data redistribution during failures. Emphasis is placed on the integration of analytical tools into streaming applications to enable real-time insights. The article outlines strategies for performance optimization and system scalability in distributed computingenvironments.