Stream Processing has evolved quickly in a short time: only a few years ago, stream processing was mostly simple real-time aggregations with limited throughput and consistency. Today, many stream processing applications have sophisticated business logic, strict correctness guarantees, high performance, low latency, and maintain terabytes of state without databases. Stream processing frameworks also abstract a lot of the low-level details away, such as routing the data streams, taking care of concurrent executions, and handling various failure scenarios while ensuring correctness.

This talk will give an introduction into Apache Flink, one of the most advanced open source stream processors that powers applications in Netflix, Uber, and Alibaba among others. In particular, we will go through the use cases that Flink was designed for, explain concepts like stateful and event-time stream processing, and discuss Flink's APIs and ecosystem.