In a series exploring real-time gaming architecture with Redpanda, this post focuses on enhancing in-game monetization through personalized real-time ads, which are strategically shown to players to boost engagement and revenue. The article explains how Redpanda, a cost-effective and scalable alternative to Apache Kafka, streams player engagement data to a Quarkus-based AdRecommender microservice, which then provides relevant ads that are displayed using Streamlit. The solution involves capturing gaming events, streaming them to Redpanda, and utilizing a Kafka Streams application to process player data and recommend ads based on specific triggers like consecutive losses. This setup demonstrates how real-time data streaming can be effectively used to personalize gaming experiences, suggesting further enhancements with machine learning for even more tailored ad delivery.