Working with a distributed cache system provides leverage to make applications faster by avoiding too many database operations. Not only that, but a cache system can also play a crucial role in avoiding unnecessary repeated calculations. In this session, we're going to go to the insides and outs of data processing in Kotlin with generics, flow, data streams, and a couple of surprises, to get systems optimal. We're going to have a look at a fictional banking system and have a look at different situations where crucial decisions can be made to allow our Bank to be efficient and still remain reliable in reaching an optimal SLA. This session combines a thorough review of data processing in Kotlin and how can we apply it considering a distributed cache system like Hazelcast. We'll have a look at data grids and how can we populate them efficiently using Kotlin as the backbone of our services.