Zenreach, a company focused on enhancing customer engagement for brick-and-mortar merchants, transitioned from a Python script to Kafka Streams to improve the scalability and reliability of their walkthrough count system, which tracks the number of walk-ins generated through Zenreach Messages. The previous system, reliant on Cassandra and MongoDB, struggled under load and added read pressure to these databases, prompting the move to a streaming solution. Kafka Streams enabled Zenreach to implement a distributed system that processes Zenreach Messages and walk-in data into walkthrough counts using services like WalkthroughsGenerator and WalkthroughsMongoExporter, alleviating the need for additional infrastructure. The switch to Kafka Streams presented challenges such as handling out-of-order data and testing punctuate logic, leading to insights on effective use of Kafka's features like TopologyTestDriver for testing and the necessity of correct partitioning in state stores. The author also shared lessons learned and tips for designing robust Kafka Streams applications, emphasizing the importance of understanding Kafka Streams architecture for proper data processing and partition management.