About This Course:

Flink is a stream processing system with an added ability to do many other things, like machine learning, graph algorithms, batch processing, etc. Using Flink you can create apps that allow you to be extremely sensitive to the latest data, such as tracking spikes in payment gateway failure or triggering live stock price movements.

This program has Thirty Solved Examples for developing both batch processing and streaming apps

Course Objective:

· Multiple-stream operations: cogroup, union, comap, connect, iterate and join

· DataStream API Transformations: Map, Filter, Reduce and FlatMap

· Window operations: Tumbling, Sliding, Session and Count windows; time notion and how custom Window functions are implemented

· DataSet API transformations: reduce, map, filter, reduce the Group

· Managing System and Checkpointing fault tolerance

· Using Gelly to represent Graph data

· Usage of Flink-ML to apply ML algorithms on the fly

Audience:

· People who are familiar with Batch processing techniques such as Hadoop want to know more about Stream processing

· Engineers who are currently looking to configure end-to-end data processing pipelines that adapt to real-time changes

Prerequisite:

· You can install a Java IDE, such as IntelliJ Idea

· Java programming expertise and familiarity with the use of Java frameworks

· Creating Jars with Maven, debugging and compiling Java code