Modern Stream Processing With Apache Flink
In our fast moving world it becomes more and more important for companies to gain near real-time insights from their data to make faster decisions. These insights do not only provide a competitve edge over ones rivals but also enable a company to create completely new services and products. Amongst others, predictive user interfaces and online recommendation can be implemented when being able to process large amounts of data in real-time.
Apache Flink, one of the most advanced open source distributed stream processing platforms, allows you to extract business intelligence from your data in near real-time. With Apache Flink it is possible to process billions of messages with milliseconds latency. Moreover, its expressive APIs allow you to quickly solve your problems, ranging from classical analytical workloads to distributed event-driven applications.
In this talk, I will introduce Apache Flink and explain how it enables users to develop distributed applications and process analytical workloads alike. Starting with Flink’s basic concepts of fault-tolerance, statefulness and event-time aware processing, we will take a look at the different APIs and what they allow us to do. The talk will be concluded by demonstrating how we can use Flink’s higher level abstractions such as FlinkCEP and StreamSQL to do declarative stream processing.