Universidade Autónoma de Lisboa - Departamento de Ciências e Tecnologias

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?

1. Understanding Spouts and Bolts, which are the building blocks of every Storm topology
2. Running a Storm topology in the local mode and in the remote mode
3. Parallelizing data processing within a topology using different grouping strategies: Shuffle grouping, Fields grouping, Direct grouping, All grouping, Custom grouping
4. Managing reliability and fault-tolerance within Spouts and Bolts
5. Performing complex transformations on the fly using the Trident topology: Map, Filter, Windowing, and Partitioning operations
6. 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