Apache Flink has become the engine powering many streaming platforms at companies like Uber and Alibaba. The benefits of Flink are well documented including it’s streaming first design allowing for low-latency streaming. Flink supports deployments with many resource managers including YARN, Mesos, and Kubernetes. Deploying on Kubernetes has many benefits including making Flink “Cloud Native.” Cloud Native deployments benefit from (1) Log aggregation (2) Tracing (3) Containers and (4) extensibility, among other things. This talk is about making Flink Cloud Native and the benefits that come as a result. These benefits include distributed tracing, log aggregation, service mesh capabilities and new patterns of exposing Flink’s APIs. In this talk, I walk through the process of taking low-level constructs from Flink and plugging them into the Cloud Native ecosystem. I’ll walk through getting distributed tracing, log aggregation and a service mesh working with Apache Flink on Kubernetes. I do this with a demo of live streaming data produced from automobile telemetry data. Attendees can expect to learn about the benefits of Cloud Native deployments of Flink and how to get started with them. They will also walk away with new patterns that can be used in Flink deployments.