You have learned about Kafka event sourcing with streams and using Kafka as a database, but you may be having a tough time wrapping your head around what that means and what challenges you will face. Kafka’s exactly once semantics, data retention rules, and stream DSL make it a great database for real-time transaction processing. This talk will focus on how to use Kafka events as a database. We will talk about using KTables vs GlobalKTables, and how to apply them to patterns we use with traditional databases. We will go over a real-world example of joining events against existing data and some issues to be aware of. We will finish covering some important things to remember about state stores, partitions, and streams to help you avoid problems when your data sets become large.