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Modern Big Data Processing with Hadoop

You're reading from   Modern Big Data Processing with Hadoop Expert techniques for architecting end-to-end big data solutions to get valuable insights

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781787122765
Length 394 pages
Edition 1st Edition
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Concepts
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Authors (3):
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 R Patil R Patil
Author Profile Icon R Patil
R Patil
 Shindgikar Shindgikar
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Shindgikar
 Kumar Kumar
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Kumar
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Toc

Table of Contents (17) Chapters Close

Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
1. Enterprise Data Architecture Principles 2. Hadoop Life Cycle Management FREE CHAPTER 3. Hadoop Design Consideration 4. Data Movement Techniques 5. Data Modeling in Hadoop 6. Designing Real-Time Streaming Data Pipelines 7. Large-Scale Data Processing Frameworks 8. Building Enterprise Search Platform 9. Designing Data Visualization Solutions 10. Developing Applications Using the Cloud 11. Production Hadoop Cluster Deployment Index

Exploring HDFS architecture


The HDFS architecture is based on master and slave patterns. NameNode is a master node and all DataNodes are SlaveNodes. Following are some important points to be noted about these two nodes.

Defining NameNode

The NameNode is a master node of all DataNodes in the Hadoop cluster. It stores only the metadata of files and directories stored in the form of a tree. The important point is NameNode never stores any other data other than metadata. NameNode keeps track of all data written to DataNodes in the form of blocks. The default block size is 256 MB (which is configurable). Without the NameNode, the data on the DataNodes filesystem cannot be read. The metadata is stored locally on the NameNode using two files—filesystem namespace image file, FSImage, and edit logs. FSImage is the snapshot of the filesystem from the start of the NameNode edit logs—all the changes of the filesystem since the NameNode started, when the NameNode starts, it reads FSImage file and edits...

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