In a world where there is an ever growing shift towards event driven streaming data, Kafka is firmly embedded in the epicenter of any Data Platform’s central nervous system. In an attempt to aide in the shift of analytics towards true event time, we have implemented a pure Kappa architecture - effectively turning the database inside out. Through extending the concept of a truly idempotent stream of events, Kafka has been elevated to the source of truth. We have eliminated extra network trips for joins as well as querying state which has significantly improved processing performance while also reducing processing latency. Tune in to discuss challenges, tips and lessons learned while implementing a pure Kappa Architecture. I will address hurdles such as scaling, warm standbys, schema evolution, and batch replay strategies - highlighting issues prevalent with any streaming Kappa based architecture. Streaming big data in and of itself comes with its own set of challenges - such as serialization formats, encryption, and strategies to efficiently utilize message headers. I invite each and every one of you to embark on a journey discussing a means to an end - resulting in processing billions of records each day.