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Mastering Hadoop

You're reading from   Mastering Hadoop Go beyond the basics and master the next generation of Hadoop data processing platforms

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Product type Paperback
Published in Dec 2014
Publisher
ISBN-13 9781783983643
Length 374 pages
Edition 1st Edition
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Author (1):
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Sandeep Karanth Sandeep Karanth
Author Profile Icon Sandeep Karanth
Sandeep Karanth
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Table of Contents (15) Chapters Close

Preface 1. Hadoop 2.X FREE CHAPTER 2. Advanced MapReduce 3. Advanced Pig 4. Advanced Hive 5. Serialization and Hadoop I/O 6. YARN – Bringing Other Paradigms to Hadoop 7. Storm on YARN – Low Latency Processing in Hadoop 8. Hadoop on the Cloud 9. HDFS Replacements 10. HDFS Federation 11. Hadoop Security 12. Analytics Using Hadoop A. Hadoop for Microsoft Windows Index

The data model


Hive data is organized as databases. A database is a logical collection of Hive tables. A database within Hive assigns a namespace for its tables. If no namespace is assigned to Hive tables, it belongs to the default namespace. The creation of a database results in the creation of an HDFS directory for the files in the database. This directory serves as the namespace for the tables. The CREATE DATABASE MasteringHadoop command creates a MasteringHadoop database. When we list the HDFS directory structure, we see a directory created for this database, as shown:

drwxr-xr-x   - sandeepkaranth supergroup          0 2014-05-15 08:55 /user/hive/warehouse/masteringhadoop.db

A table is the basic unit of data storage similar to traditional RDBMS. It logically groups records of the same type. Records are rows corresponding to typed columns. A table maps to a single directory within HDFS. Hive also allows imposing structures on existing data locations via external tables. Metadata stored...

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