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

Product type Paperback

Published in Feb 2019

Publisher Packt

ISBN-13 9781788620444

Length 544 pages

Edition 1st Edition

Languages

Java

Tools

Hadoop

Concepts

Big Data

Authors (3):

Timothy Wong

Manish Kumar

Chanchal Singh

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Table of Contents (21) Chapters

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

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Erasure encoding in Hadoop 3.x

HDFS achieves fault-tolerance by replicating each block three times by default. However, in big clusters, the replication factor can be more. The purpose of replication is to handle data loss against machine failure, providing data locality for the MapReduce job, and so on. The replication takes more storage space, which means that if our replication factor is three, HDFS will take an extra 200% space to store file. In short, storing 1 GB of data will require 3 GB of memory. This also causes metadata memory on NameNode.

HDFS introduced erasure coding (EC) for storing data by taking less storage space. Now, data is labelled based on their usage access pattern, and after the conditions for erasure coding have been satisfied, data will be applicable for erasure coding. The term Data Temperature is used to identify the data usage pattern. The different...

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Authors (3)

Singh

Contacted on 12/01/18 by Davis Anto

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Wong

Dr. Timothy Wong is a 30 years IT veteran. He holds a PhD in networking from University of Manchester Institute of Science and Technology, UK. He has deep experience in wireless and wireline networking, digital a/v, software development, sales and consulting. He frequently works with telcos, banks, governments and enterprises. He is also a professor on Big Data, Wireless and IoT at Humber College, Toronto, Canada. As an entrepreneur, he co-founded a number of companies in the past 20 years. He infuses business and technical knowledge to pursue successes.

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Kumar

Ashish Kumar is a seasoned data science professional, a publisher author and a thought leader in the field of data science and machine learning. An IIT Madras graduate and a Young India Fellow, he has around 7 years of experience in implementing and deploying data science and machine learning solutions for challenging industry problems in both hands-on and leadership roles. Natural Language Procession, IoT Analytics, R Shiny product development, Ensemble ML methods etc. are his core areas of expertise. He is fluent in Python and R and teaches a popular ML course at Simplilearn. When not crunching data, Ashish sneaks off to the next hip beach around and enjoys the company of his Kindle. He also trains and mentors data science aspirants and fledgling start-ups.

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