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Mastering Hadoop 3

You're reading from   Mastering Hadoop 3 Big data processing at scale to unlock unique business insights

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Product type Paperback
Published in Feb 2019
Publisher Packt
ISBN-13 9781788620444
Length 544 pages
Edition 1st Edition
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Authors (3):
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Timothy Wong Timothy Wong
Author Profile Icon Timothy Wong
Timothy Wong
Manish Kumar Manish Kumar
Author Profile Icon Manish Kumar
Manish Kumar
Chanchal Singh Chanchal Singh
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Chanchal Singh
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Table of Contents (21) Chapters Close

Preface 1. Section 1: Introduction to Hadoop 3 FREE CHAPTER
2. Journey to Hadoop 3 3. Deep Dive into the Hadoop Distributed File System 4. YARN Resource Management in Hadoop 5. Internals of MapReduce 6. Section 2: Hadoop Ecosystem
7. SQL on Hadoop 8. Real-Time Processing Engines 9. Widely Used Hadoop Ecosystem Components 10. Section 3: Hadoop in the Real World
11. Designing Applications in Hadoop 12. Real-Time Stream Processing in Hadoop 13. Machine Learning in Hadoop 14. Hadoop in the Cloud 15. Hadoop Cluster Profiling 16. Section 4: Securing Hadoop
17. Who Can Do What in Hadoop 18. Network and Data Security 19. Monitoring Hadoop 20. Other Books You May Enjoy

Storm/Heron

Apache Storm is a distributed real-time computing system that processes a large volume of high velocity data. Unlike Spark, it does not create micro batches instead, it gives us the ability to process events in real-time mode. Applications where even a delay of seconds can cause a big loss could not go with a near real-time processing engine like Spark. There are a large number of companies that are using Apache Storm for processing events in real time for use cases such as fraud detection, recommendation engines, identifying suspicious activity, and so on. 

Apache Storm is a distributed real-time processing engine that provides the ability to process events as quick as they arrive to the node. It is super fast and can process millions of events per machine. Storm is reliable in processing both bounded and unbounded streams of event. 
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