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Learning Hadoop 2

You're reading from Learning Hadoop 2 Design and implement data processing, lifecycle management, and analytic workflows with the cutting-edge toolbox of Hadoop 2

Product type Paperback

Published in Feb 2015

Publisher Packt

ISBN-13 9781783285518

Length 382 pages

Edition 1st Edition

Tools

Hadoop

Concepts

Data Processing

Table of Contents (13) Chapters

Preface

1. Introduction

2. Storage FREE CHAPTER

3. Processing – MapReduce and Beyond

4. Real-time Computation with Samza

5. Iterative Computation with Spark

6. Data Analysis with Apache Pig

7. Hadoop and SQL

8. Data Lifecycle Management

9. Making Development Easier

10. Running a Hadoop Cluster

11. Where to Go Next

Index

Chapter 7. Hadoop and SQL

MapReduce is a powerful paradigm that enables complex data processing that can reveal valuable insights. As discussed in earlier chapters however, it does require a different mindset and some training and experience on the model of breaking processing analytics into a series of map and reduce steps. There are several products that are built atop Hadoop to provide higher-level or more familiar views of the data held within HDFS, and Pig is a very popular one. This chapter will explore the other most common abstraction implemented atop Hadoop: SQL.

In this chapter, we will cover the following topics:

What the use cases for SQL on Hadoop are and why it is so popular
HiveQL, the SQL dialect introduced by Apache Hive
Using HiveQL to perform SQL-like analysis of the Twitter dataset
How HiveQL can approximate common features of relational databases such as joins and views
How HiveQL allows the incorporation of user-defined functions into its queries
How SQL on Hadoop...

The rest of the chapter is locked

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