Event stream processing is a powerful technology that solves many business challenges, including big data ingestion, data integration, and real-time data processing. It allows for the analysis of big data streams in-motion as it's generated, opening up new use cases for organizations across various industries. Two popular tools for building event streaming applications within the Apache Kafka ecosystem are ksqlDB and Kafka Streams. While both provide solutions for stream processing workloads, they have key differences to be aware of. ksqlDB is an event streaming database built on top of Kafka Streams that provides a high-level SQL syntax for building streams and tables, creating materialized views, and managing Kafka connectors without worrying about traditional application management concerns. Kafka Streams, on the other hand, is a Java virtual machine client library for building elastic and fault-tolerant stream processing applications on top of Kafka. It allows developers to build stream processing workloads with the full power of any JVM-based language. The choice between ksqlDB and Kafka Streams often boils down to trade-offs between control and simplicity. By using these technologies, organizations can harness the power of real-time data processing to power their AI projects with real-time data.