Applying Real-time Processing Using Apache Storm

Pluralsight

Course Summary

Storm lets you to work with large scale streaming data using it's distributed real-time processing architecture. This course discusses the components of Storm topologies and how to use Storm for applying machine learning in real-time.

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Course Description

Storm is meant to be to used for distributed real-time processing, the way Hadoop is used for distributed batch processing. With Storm, you can process informations such as trends and breaking news and react to it in real-time. In this course, Applying Real-time Processing Using Apache Storm, you'll learn how to apply Storm for real-time processing. First, you'll discover how to set up a data processing pipeline using Storm topologies. Next, you'll explore parallelization by controlling data flows between components. Then, you'll cover how to perform complex data transforms using the Trident API. Finally, you'll learn how to apply machine learning models in real-time. By the end of this course, you'll be able to build your own Storm applications for different real-time processing tasks.

Course Description

Storm is meant to be to used for distributed real-time processing, the way Hadoop is used for distributed batch processing. With Storm, you can process informations such as trends and breaking news and react to it in real-time. In this course, Applying Real-time Processing Using Apache Storm, you'll learn how to apply Storm for real-time processing. First, you'll discover how to set up a data processing pipeline using Storm topologies. Next, you'll explore parallelization by controlling data flows between components. Then, you'll cover how to perform complex data transforms using the Trident API. Finally, you'll learn how to apply machine learning models in real-time. By the end of this course, you'll be able to build your own Storm applications for different real-time processing tasks.

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Course Syllabus

Course Overview
- 1m 29s

—Course Overview 1m 29s

Understanding the Components of Storm
- 35m 59s

—Contrasting Real-time and Batch Processing 5m 36s
—Understanding the Components of Storm 5m 15s
—Representing Data in Storm Components 3m 45s
—Building a Hello World Topology 4m 13s
—Implementing the Spout 5m 30s
—Implementing the Bolt 6m 2s
—Running the Topology 5m 35s

Parallelizing Data Processing Using Storm Components
- 32m 44s

—Understanding Parallelism in a Storm Cluster 3m 49s
—Setting up a Remote Cluster 4m 32s
—Running a Topology on a Remote Cluster 5m 4s
—Controlling Data Flow with Stream Grouping 4m 28s
—Building a Word Count Topology 1m 46s
—Implementing the Topology Components 4m 26s
—Contrasting Different Stream Grouping Strategies 4m 9s
—Implementing a Custom Stream Grouping 4m 26s

Customizing Storm Components for Better Reliability
- 16m 56s

—Guaranteeing Message Processing in Storm 5m 13s
—Managing Reliability in Storm Components 3m 13s
—Implementing Reliability in a Spout 5m 22s
—Implementing Reliability in a Bolt 3m 7s

Querying Storm Data Streams Using Trident
- 25m 13s

—Processing Data Using Trident 4m 20s
—Implementing a Trident Topology to Process Text 6m 52s
—Aggregating Data Streams 4m 2s
—Using Trident for Distributed Remote Procedure Calls 3m 53s
—Maintaining and Querying State 2m 23s
—Querying Word Counts 3m 41s

Applying Machine Learning to Storm Data Streams
- 22m 38s

—Understanding the Machine Learning Process 5m 30s
—Integrating Storm with Python 5m 22s
—Predicting Sentiment in Real-time 3m 11s
—Training a Sentiment Analysis Model in Python 5m 27s
—Building a Storm Topology to Predict Sentiment 3m 7s