This study examines architectural strategies for reprocessing historical data in real-time systems built around the Kappa architecture and Apache Kafka–based microservices. The research addresses the growing need to recompute derived state, machine-learning features and aggregates without interrupting continuous processing or violating correctness guarantees. The work systematises approaches to “time-travel” over event logs, including full-topic replay, snapshot-plus-log reconstruction and isolated backfill pipelines. Special attention is given to the interaction between Kafka, stateful stream processors such as Apache Flink and Kafka Streams, and microservice-oriented designs that rely on local or external state stores. The goal is to formulate practical design guidelines for architecting reprocessing workflows under strict latency, availability and consistency requirements. The article presents an analytical comparison of modern stream-processing platforms and real-world case studies from the financial and fraud detection domains. In conclusion, the study formulates recommendations on choosing between local and external state, structuring replay traffic, and integrating reprocessing pipelines into production Kappa-style systems without global downtime.

Kappa architecture, Apache Kafka, real-time stream processing, historical data reprocessing, time-travel replay, Kafka Streams, Apache Flink, stateful microservices, event sourcing, data streaming architecture

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