Machine Learning : Apache Storm - Learn by Example
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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
Chapter 01: You, This Course, and Us 02:06
Chapter 02: Stream Processing with Storm 25:29
How does Twitter compute Trends?
Improving Performance using Distributed Processing
Building blocks of Storm Topologies
Adding Parallelism in a Storm Topology
Components of a Storm Cluster
Chapter 03: Implementing a Hello World Topology 25:20
A Simple Hello World Topology
Ex 1: Implementing a Spout
Ex 1: Implementing a Bolt
Ex 1: Submitting the Topology
Chapter 04: Processing Data using Files 34:08
Ex 2: Reading Data from a File
Representing Data using Tuples
Ex 3: Accessing data from Tuples
Ex 4: Writing Data to a File
Chapter 05: Running a Topology in the Remote Mode 14:42
Setting up a Storm Cluster
Ex 5: Submitting a topology to the Storm Cluster
Chapter 06: Adding Parallelism to a Storm Topology 24:36
Ex 6 : Shuffle Grouping
Ex 7: Fields Grouping
Ex 8: All Grouping
Ex 9: Custom Grouping
Ex 10: Direct Grouping
Chapter 07: Building a Word Count Topology 10:04
Ex 11: Building a Word Count Topology
Chapter 08: Remote Procedure Calls Using Storm 12:48
Ex 12: A Storm Topology for DRPC calls
Chapter 09: Managing Reliability of Topologies 10:31
Ex 13: Managing Failures in Spouts
Chapter 10: Integrating Storm with Different Sources/Sinks 15:33
Ex 14: Implementing a Twitter Spout
Ex 15: Using a HDFS Bolt
Chapter 11: Using the Storm Multilang Protocol 08:24
Ex 16: Building a Storm Topology using Python
Chapter 12: Complex Transformations using Trident 01:00:05
Ex 17: Building a basic Trident Topology rs Classifier
Ex 18: Implementing a Map Function
Ex 19: Implementing a Filter Function
Ex 20: Aggregating data Classifiers
Ex 21: Understanding States
Ex 21: Understanding States
Ex 23: Joining data streams
Ex 24: Building a Twitter Hashtag Extractor
Length of Subscription: 12 Months Online On-Demand Access Running Time: 4 hrs 4 min Platform: Windows & MAC OS Level: Beginner to Advanced Project Files: Included
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