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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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Toc

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

Mahout

Mahout is the Apache library for open source learning. Mahout mainly uses, but is not limited to, the classification and dimensional algorithms of clustering recommend engines (collaborative filtering and classification). Mahout's objective is to provide the usual machine learning algorithms with a highly scalable implementation. If the historical data to be used is large, then Mahout is the machinery of choice. We generally find that it is not possible to process the data on a single device. With large data becoming an important area of focus, Mahout meets the need for a machine learning tool that can extend beyond a single computer. Mahout is different from other tools such as R, Weka, and so on, as its emphasis on scalability. The Mahout learning implementations are written in Java, and most but not all of them are compiled using the MapReduce paradigm...

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