Data Consistency in Distributed Multi-Stage Event Processing Pipelines

The article examines the problem of ensuring end-to-end data consistency in distributed multi-stage event processing pipelines, which are actively used in modern real-time systems. The relevance of the study is determined by the rapid growth of streaming analytics needs and the widespread use of Apache Kafka, making message latency, duplication, and disorder critical factors for industries ranging from fintech to IoT. The goal of this work is to propose a formal model that unifies an extended event representation and a set of invariants that guarantee correct processing even in the presence of component failures. The novelty of the approach lies in the formalization of an event as a tuple ⟨id, tsₛᵣ????, p, v, σ⟩, where id is responsible for deduplication, tsₛᵣ???? records the time of occurrence, p specifies the partition, v is the payload, and σ is the schema version, which enables ordering recovery and supports format evolution. The pipeline is modeled as a directed acyclic graph (DAG) of operators having the properties of determinism, idempotence, and monotonicity. CRDT aggregates are used for convergence in duplication; SLA alerts from watermark mechanisms are used to minimize data loss. The main findings indicate that, under specified conditions, the system can tolerate delays, failures, and redeliveries without compromising consistency. Extended events and formal operators enable state recovery; stream semantics are ensured by four invariants. This research is particularly relevant for professionals designing and operating real-time event-driven systems, stream processing applications, microservices architectures, and high-integrity data integration pipelines.