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25 Solved examples on Real Time Stream Processing
What you'll learn:- 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
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 buildingStorm Applications.
What's covered?
1)UnderstandingSpoutsandBoltswhich are the building blocks of every Storm topology.
2)Runninga Storm topology in thelocal modeand in theremote mode
3)Parallelizingdata processing within a topology using different grouping strategies: Shuffle grouping, fields grouping, Direct grouping, All grouping, Custom Grouping
4) Managingreliability and fault-tolerancewithin Spouts and Bolts
5)Performingcomplex transformations on the flyusing theTrident topology : Map, Filter, Windowing and Partitioning operations
6)Applying ML algorithms on the fly using libraries likeTrident-ML and Storm-R.
Overview
