In the era of streaming data, tools like Redpanda and Faust are revolutionizing data handling by enabling real-time stream processing. Redpanda, a streaming platform, allows users to set up clusters quickly, while Faust, a Python-based stream-processing library, integrates seamlessly with Redpanda to process data in real-time. Faust, originally developed by Robinhood engineers, facilitates the creation of pipelines that process data from Kafka topics, leveraging Python's ecosystem of data libraries like Pandas and NumPy. One practical application demonstrated is the calculation of a rolling average for temperature data from various sensors, utilizing Faust's abstractions such as streams and tables, and its storage engine, RocksDB, for optimal performance. The tutorial guides through setting up a Redpanda cluster, defining Faust applications with agents, topics, and tables, and implementing a data generator to simulate sensor data. This approach showcases the ease and efficiency of building stream processing applications, highlighting Faust's fault tolerance and state persistence capabilities. The complete code for this tutorial is accessible on GitHub, providing users with a hands-on opportunity to explore stream processing with Faust and Redpanda.