Data Transformations with Apache Pig

Pig is an open source engine for executing parallelized data transformations which run on Hadoop. This course shows you how Pig can help you work on incomplete data with an inconsistent schema, or perhaps no schema at all.
Course info
Rating
(29)
Level
Beginner
Updated
May 12, 2017
Duration
3h 15m
Table of contents
Course Overview
Introducing Pig
Using the GRUNT Shell
Loading Data into Relations
Working with Basic Data Transformations
Working with Advanced Data Transformations
Executing MapReduce Using Pig
Description
Course info
Rating
(29)
Level
Beginner
Updated
May 12, 2017
Duration
3h 15m
Description

Pig is an open source software which is part of the Hadoop eco-system of technologies. Pig is great at working with data which are beyond traditional data warehouses. It can deal well with missing, incomplete, and inconsistent data having no schema. In this course, Data Transformations with Apache Pig, you'll learn about data transformations with Apache. First, you'll start with the very basics which will show you how to get Pig installed and get started working with the Grunt shell. Next, you'll discover how to load data into relations in Pig and store transformed results to files via load and store commands. Then, you'll work on a real world dataset where you analyze accidents in NYC using collision data from the City of New York. Finally, you'll explore advanced constructs such as the nested foreach and also gives you a brief glimpse into the world of MapReduce and shows you how easy it is to implement this construct in Pig. By the end of this course, you'll have a better understanding of data transformations with Apache Pig.

About the author
About the author

A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.

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