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?
What are the requirements?
- 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 am I going to get from this course?
- Experience in Java programming and familiarity with using Java frameworks
- A Java IDE such as IntelliJ Idea should be installed
What is the target audience?
- 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
- 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
Chapter 03: Implementing a Hello World Topology 25:20
- 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 04: Processing Data using Files 34:08
- A Simple Hello World Topology
- Ex 1: Implementing a Spout
- Ex 1: Implementing a Bolt
- Ex 1: Submitting the Topology
Chapter 05: Running a Topology in the Remote Mode 14:42
- 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 06: Adding Parallelism to a Storm Topology 24:36
- Setting up a Storm Cluster
- Ex 5: Submitting a topology to the Storm Cluster
Chapter 07: Building a Word Count Topology 10:04
- Ex 6 : Shuffle Grouping
- Ex 7: Fields Grouping
- Ex 8: All Grouping
- Ex 9: Custom Grouping
- Ex 10: Direct Grouping
Chapter 08: Remote Procedure Calls Using Storm 12:48
- Ex 11: Building a Word Count Topology
Chapter 09: Managing Reliability of Topologies 10:31
- Ex 12: A Storm Topology for DRPC calls
Chapter 10: Integrating Storm with Different Sources/Sinks 15:33
- Ex 13: Managing Failures in Spouts
Chapter 11: Using the Storm Multilang Protocol 08:24
- Ex 14: Implementing a Twitter Spout
- Ex 15: Using a HDFS Bolt
Chapter 12: Complex Transformations using Trident 01:00:05
- Ex 16: Building a Storm Topology using Python
- 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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