The text explores the use of Apache Kafka and ksqlDB to process and query streaming data without relying on external datastores. It describes a system that captures Wi-Fi packets and processes them to provide real-time information about connected devices via a Telegram bot, showcasing ksqlDB's ability to create stateful aggregates and perform pull and push queries. The approach highlights Kafka's role as a distributed, scalable, and fault-tolerant platform that can serve as both a message broker and a data store, further enhanced by ksqlDB's SQL-based stream processing capabilities. The article also delves into integrating MongoDB with Kafka to enrich data streams and emphasizes the flexibility of event-driven architectures in building systems that respond to real-time data, demonstrating how ksqlDB's materialized views and integration with other data sources can simplify complex query operations and data management.