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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

Chapter 4. Advanced Hive

SQL is a popular data-processing language that has been around for four decades. There are scores of people who are already familiar with Relational Data Stores and SQL. A natural step in onboarding more users onto Hadoop is to flatten the learning curve by bringing in concepts they are well versed with. Hive introduces relational and SQL concepts into Hadoop MapReduce. In the chapter on Pig, you saw the advanced usage of Pig scripts to author MapReduce workflows. In this chapter, we will delve into the advanced usage of Hive.

Apache Hive is often described as a data warehouse infrastructure. Traditionally, business intelligence is gathered from a data warehouse, a database that stores data from many sources within an enterprise. This database stores both historical and current data in an enterprise. This data store is primarily queried for reporting and analytics. Traditionally, infrastructure that made data warehouses consisted of Relational Databases...

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