Synopsis How often were you told that you have to stream data at scale, process and analyze them to make real time decision making without losing a single event? How often are you told that the scale of the data we are talking about is in several billions and the cost of one message can go up to 10s of 1000s of dollars? How often did you have to deal with the challenge of doing real-time decision making, analytics, ML and auditing with data in motion leave apart the data at rest? Real Time Inventory and Replenishment System We have the requirement to develop a system that will enable us to do real time tracking of items moving within the supply chain as it is vital for making quicker replenishment decisions and other real-time use cases. To fulfill this requirement, we chose to build an event-driven system that will track this inventory information and create plans and orders in near real time. We have events at the heart of the systems. Through this journey to meet scale with reliability, we learnt a lot of lessons to leverage Kafka at scale and various optimized ways to produce and consume from Kafka. We look forward to meet you all and discuss in detail our journey and connect you with solutions to some of your problems. Key takeaways: – Leveraging kafka and the related ecosystem on Openstack and Azure – Saving Cost at scale with Kafka and related eco-system – Scaling Kafka Streams and Kafka Connector applications. – Tuning Kafka Streams to improve performance. – How to stabilize the kafka connectors operating at scale.