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Hadoop Real-World Solutions Cookbook- Second Edition

You're reading from   Hadoop Real-World Solutions Cookbook- Second Edition Over 90 hands-on recipes to help you learn and master the intricacies of Apache Hadoop 2.X, YARN, Hive, Pig, Oozie, Flume, Sqoop, Apache Spark, and Mahout

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Product type Paperback
Published in Mar 2016
Publisher
ISBN-13 9781784395506
Length 290 pages
Edition 2nd Edition
Tools
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Author (1):
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Tanmay Deshpande Tanmay Deshpande
Author Profile Icon Tanmay Deshpande
Tanmay Deshpande
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Toc

Table of Contents (12) Chapters Close

Preface 1. Getting Started with Hadoop 2.X FREE CHAPTER 2. Exploring HDFS 3. Mastering Map Reduce Programs 4. Data Analysis Using Hive, Pig, and Hbase 5. Advanced Data Analysis Using Hive 6. Data Import/Export Using Sqoop and Flume 7. Automation of Hadoop Tasks Using Oozie 8. Machine Learning and Predictive Analytics Using Mahout and R 9. Integration with Apache Spark 10. Hadoop Use Cases Index

Performing JOINS in Pig


In this recipe, we will learn how to perform various joins in Pig in order to join datasets.

Getting ready

To perform this recipe, you should have a running Hadoop cluster as well as the latest version of Pig installed on it.

How to do it...

JOIN operations are very famous in SQL. Pig Latin also supports joining datasets based on a common attribute between them. Pig supports both Inner and Outer joins. Let's understand these syntaxes one by one.

In order to learn about Joins in Pig, we'll need two datasets. The first one is the employee dataset, which we have been using in earlier recipes, the second is the ID location dataset, which contains information about the ID of an employee and their location.

The employee dataset will look like this:

1	Tanmay	ENGINEERING	5000
2	Sneha	PRODUCTION	8000
3	Sakalya	ENGINEERING	7000
4	Avinash	SALES	6000
5	Manisha	SALES	5700
6	Vinit	FINANCE	6200

The ID location dataset will look like this:

1	Pune
2	London
3	Mumbai
4	Pune

Like the emps data...

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