The proliferation of the Internet of Things (IoT) has led to a significant increase in data-centric applications, primarily driven by the collection and real-time processing of telemetry data from various sensors. This text outlines a tutorial for building a real-time IoT telemetry data processing pipeline using Apache Spark and Redpanda, aimed at monitoring temperature-sensitive goods in cold storage. By simulating an IoT setup where temperature data from multiple sensors is streamed through a pipeline, the tutorial demonstrates the use of Docker containers for infrastructure deployment, PySpark for data processing, and Python scripts for simulation and visualization. The processed data is displayed using Matplotlib, providing insights into individual and average temperature readings from the sensors. This setup exemplifies how IoT telemetry data can be leveraged for applications like resource consumption analysis, real-time monitoring, predictive maintenance, and more, showcasing the benefits of combining affordable sensor technology and open protocols in optimizing operations across various industries.