In this chapter, we acquainted the reader with Flink architecture. We discussed KAPPA architecture and how Flink works. There are different sources and sinks available with Flink. Examples of sources such as Kafka and RabbitMQ were explained. Examples of sinks such as Cassandra were explained with Kafka as a source. Flink gives us DataSet and DataFrame API for stream and batch processing respectively. We explained the different transformations available with each API. There are two advanced level libraries provided by Flink: CEP and Gelly. CEP is used for real-time processing with pattern implementations. Gelly is a graph API over Flink. In the end, we have given the reader problems to solve for themselves.
In the next chapter, we will see how to develop a real example using the applications of all this book's scenarios.