Real-time anomaly detection is essential for identifying unexpected patterns and critical events in streaming data. This talk addresses the unique algorithmic challenges of anomaly detection in streaming environments and introduces a new feature within Apache Beam designed for this purpose. We will demonstrate how to seamlessly integrate both online and pre-trained offline anomaly detection models into Beam pipelines, empowering users to build robust, scalable, and real-time anomaly detection systems.