Leveraging Timely Dataflow and Redpanda, building real-time, scalable, fault-tolerant data processing systems has become more accessible. Timely Dataflow, a low-latency cyclic dataflow computational model, enables the construction of data-parallel systems that can scale from a single thread to a cluster, making it ideal for real-time anomaly detection. Redpanda, compatible with Apache Kafka APIs, facilitates real-time data monitoring across multiple sources. By integrating Bytewax, a Python binding for Rust-based Timely Dataflow, with Redpanda, developers can efficiently create applications in Python. The process involves Bytewax reading sensor data from a Redpanda topic, calculating anomalies using a five-second data aggregation window, and then outputting results to another topic. This setup involves generating mock air quality data, processing it with an online anomaly detection algorithm, and ensuring the system's real-time capabilities via Docker and Redpanda's API. The combination of Redpanda's Kafka-compatibility and Bytewax's performance provides a robust solution for both small-scale projects and extensive production environments.