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.
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Course Syllabus
Course Overview- 1m 29s
—Course Overview 1m 29sUnderstanding 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 35sParallelizing Data Processing Using Storm Components- 32m 44sCustomizing Storm Components for Better Reliability- 16m 56sQuerying Storm Data Streams Using Trident- 25m 13sApplying Machine Learning to Storm Data Streams- 22m 38s
