Flink 1.X's architecture consists of various components such as deploy, core processing, and APIs. We can easily compare the latest architecture with Stratosphere's architecture and see its evolution. The following diagram shows the components, APIs, and libraries:

Flink has a layered architecture where each component is a part of a specific layer. Each layer is built on top of the others for clear abstraction. Flink is designed to run on local machines, in a YARN cluster, or on the cloud. Runtime is Flink's core data processing engine that receives the program through APIs in the form of JobGraph. JobGraph is a simple parallel data flow with a set of tasks that produce and consume data streams.

The DataStream and DataSet APIs are the interfaces programmers can use for defining the Job. JobGraphs are generated by these APIs when the programs are compiled. Once compiled, the DataSet API allows the optimizer to generate the optimal execution plan while DataStream API uses a stream build for efficient execution plans.

The optimized JobGraph is then submitted to the executors according to the deployment model. You can choose a local, remote, or YARN mode of deployment. If you have a Hadoop cluster already running, it is always better to use a YARN mode of deployment.