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Apache Hadoop 3 Quick Start Guide

You're reading from   Apache Hadoop 3 Quick Start Guide Learn about big data processing and analytics

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Product type Paperback
Published in Oct 2018
Publisher Packt
ISBN-13 9781788999830
Length 220 pages
Edition 1st Edition
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Author (1):
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Hrishikesh Vijay Karambelkar Hrishikesh Vijay Karambelkar
Author Profile Icon Hrishikesh Vijay Karambelkar
Hrishikesh Vijay Karambelkar
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Table of Contents (10) Chapters Close

Preface 1. Hadoop 3.0 - Background and Introduction FREE CHAPTER 2. Planning and Setting Up Hadoop Clusters 3. Deep Dive into the Hadoop Distributed File System 4. Developing MapReduce Applications 5. Building Rich YARN Applications 6. Monitoring and Administration of a Hadoop Cluster 7. Demystifying Hadoop Ecosystem Components 8. Advanced Topics in Apache Hadoop 9. Other Books You May Enjoy

Advanced Hadoop data storage file formats

We have looked at different formats supported by HDFS in Chapter 3, Deep Dive into the Hadoop Distributed File System. We covered many formats including SequenceFile, Map File, and the Hadoop Archive format. We will look at more formats now. The reason why they are covered in this section is because these formats are not used by Apache Hadoop or HDFS directly; they are used by the ecosystem components. Before we get into the format, we must understand the difference between row-based and columnar-based databases because ORC and Parquet formats are columnar data storage formats. The difference is in the way the data gets stored in the storage device. A row-based database stores data in row format, whereas a columnar database stores it column by column. The following screenshot shows how the storage patterns differ between these types:

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