The paper examines real-time log analytics for distributed, cloud-native back-end systems, where operational decisions depend on the rapid recognition of critical runtime conditions. The relevance follows from the latency sensitivity of microservice-based finance and trading workloads, where propagation of failures, retries, and cascading timeouts rapidly degrades user-facing and internal processing. The novelty lies in an integrated analytical synthesis that ties stream-processing scalability evidence, tracing-tool capabilities, monitoring-tool taxonomies, instrumentation overhead studies, and modern log-anomaly detection research into one consistent engineering narrative. The study aims to develop a low-latency detection approach based on peer-reviewed findings. To achieve this goal, the work employs a systematic selection of recent literature, structured extraction of architectural patterns, and comparative reasoning across the ingestion, correlation, detection, and alerting stages. The analysis encompasses distributed stream processing benchmarks, near-real-time processing in practical architectures, runtime verification for streaming systems, and state-of-the-art log anomaly detection methods. The closing part derives design implications for practitioners building observability and incident-response pipelines.

real-time log analytics, distributed systems, stream processing, observability, microservices, critical event detection, low latency, tracing, anomaly detection, cloud platforms

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