We present a (soo) real-time event-based anomaly detection application for manufacturing equipment, built on top of the general purpose stream processing framework Apache Flink. e anomaly detection involves multiple CPUs and/or memory intensive tasks, such as clustering on large time-based window and parsing input data in RDF-format. e main goal is to reduce end-to-end latencies, while handling high input throughput and still provide exact results. Given a truly distributed seeing, this challenge also entails careful task and/or data parallelization and balancing. We propose FlinkMan, a system that ooers a generic and eecient solution , which maximizes the usage of available cores and balances the load among them. We illustrates the accuracy and eeciency of FlinkMan, over a 3-step pipelined data stream analysis, that includes clustering, modeling and querying.