In this session we present our journey of building a queryable real-time streaming analytics engine using the Kafka Streams API. Keeping reliability and fault tolerance in mind, we created a system, across data centers, to process millions of events per second and to provide real-time insights into user behavior on the internet. Using powerful tools like KSQL we were able to generate intuitive insights into session based metrics like active sessions, current active users, open orders, etc. Further, we funneled all our data into Druid to perform low latency OLAP queries that powers our free-form reporting engine. We also highlight the lessons we learnt along the way and describe certain metrics that were important in insuring the integrity of our Kafka cluster.