Machine learning : apache storm - learn by example

Course Description
In this ’Apache Storm: Learn by Example’ online course, you will learn how to use Storm to build applications which need you to be highly responsive to the latest data, and react within seconds and minutes, such as finding the latest trending topics on Twitter, or monitoring spikes in payment gateway failures. Supplemental material included!

Storm is to real-time stream processing what Hadoop is to batch processing. From simple data transformations to applying machine learning algorithms on the fly, Storm can do it all.

What’s covered in this Apache Storm: Learn by Example online training course?
Understanding Spouts and Bolts, which are the building blocks of every Storm topology
Running a Storm topology in the local mode and in the remote mode
Parallelizing data processing within a topology using different grouping strategies: Shuffle grouping, Fields grouping, Direct grouping, All grouping, Custom grouping
Managing reliability and fault-tolerance within Spouts and Bolts
Performing complex transformations on the fly using the Trident topology: Map, Filter, Windowing, and Partitioning operations
Applying ML algorithms on the fly using libraries like Trident-ML and Storm-R

What are the requirements?
Experience in Java programming and familiarity with using Java frameworks
A Java IDE such as IntelliJ Idea should be installed

What am I going to get from this course?
Build a Storm Topology for processing data
Manage reliability and fault tolerance of the topology
Control parallelism using different grouping strategies
Perform complex transformations using Trident
Apply Machine Learning algorithms on the fly in Storm applications

What is the target audience?
Engineers looking to set up end-to-end data processing pipelines that react to changes in real time
Folks familiar with Batch processing technologies like Hadoop who want to learn more about Stream processing