Learn By Example: Apache Storm

Applying ML algorithms on the fly using libraries like Trident-ML and Storm-R.
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Applying ML algorithms on the fly using libraries like Trident-ML and Storm-R.

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

This course has 25 Solved Examples on building Storm Applications.

Course Objective:

  • 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.
  • 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 processin
  • Experience in Java programming and familiarity with using Java frameworks
  • A Java IDE such as IntelliJ Idea should be installed
More Information
Lab AccessNo
Learning StyleSelf-Paced Learning
Course Duration4 Hours
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