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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
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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

Points to remember

We have covered the HDFS in detail and the following are a few points to remember:

  • HDFS consists of two main components: NameNode and DataNode. NameNode is a master node that stores metadata information, whereas DataNodes are slave nodes that store file blocks.
  • Secondary NameNode is responsible for performing checkpoint operations in which edit log changes are applied to fsimage. This is also known as a checkpoint node.
  • Files in HDFS are split into blocks and blocks are replicated across a number of DataNodes to ensure fault tolerance. The replication factor and block size are configurable.
  • HDFS Balancer is used to distribute data in an equal fashion between all DataNodes. It is a good practice to run balancer whenever a new DataNode is added and schedule a job to run balancer at regular intervals.
  • In Hadoop 3, high availability can now have more...
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