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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
Author Profile Icon Chanchal Singh
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

Summary

In this chapter, we learned about detailed architecture and uses of a few widely used Hadoop components, such as Apache Pig, Apache HBase, Apache Kafka, and Apache Flume. We have covered a few examples around writing custom UDF in Apache Pig, writing custom source, sink, and interceptor in Apache Flume, writing producer and consumer in Apache Kafka, and so on. Our focus was also to see how we can install and set up these componentS for practical use. 

In the next chapter, our focus will be to look into some advanced topics in Big Data and cover some useful fundamental techniques and concepts, such as compression, file format serialization techniques, and some important pillars of data governance. 

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