Real-time transaction processing has revolutionized how businesses handle data by allowing immediate analysis and response to incoming information, making it crucial for industries like finance where quick decision-making is essential. This tutorial explores a real-time transaction processing solution using Redpanda, Apache Flink, and MongoDB, which together create a robust stack capable of handling high-volume data streams with low latency. Redpanda serves as a streaming data platform with a Kafka API, while Flink offers real-time data processing capabilities, and MongoDB provides a flexible NoSQL database for high-speed data ingestion and real-time analytics. The integration of these technologies is demonstrated through a demo project that sets up a real-time transaction processing application, highlighting their ability to process and analyze transactions efficiently, thereby enabling businesses to make timely decisions and deliver enhanced customer experiences. Challenges such as ensuring data consistency and exactly-once processing are addressed, and the tutorial provides step-by-step guidance on setting up the system locally using Docker, developing a Flink application, and producing financial transaction events into Redpanda for processing and ingestion into MongoDB.