Beginning Apache Pig Big Data Processing Made Easy by Balaswamy Vaddeman

By Balaswamy Vaddeman

Discover ways to use Apache Pig to improve light-weight vast info functions simply and fast. This publication exhibits you several optimization ideas and covers each context the place Pig is utilized in significant info analytics. starting Apache Pig indicates you the way Pig is simple to profit and calls for rather little time to enhance giant information functions. The publication is split into 4 components: the entire positive aspects of Apache Pig integration with different instruments easy methods to resolve complicated enterprise difficulties and optimization of instruments. Youll become aware of themes corresponding to MapReduce and why it can't meet each company want the gains of Pig Latin corresponding to info forms for every load, shop, joins, teams, and ordering how Pig workflows will be created filing Pig jobs utilizing Hue and dealing with Oozie. Youll additionally see the best way to expand the framework by means of writing UDFs and customized load, shop, and filter out capabilities. ultimately youll disguise diverse optimization options reminiscent of amassing facts a couple of Pig script, becoming a member of thoughts, parallelism, and the position of information codecs in sturdy functionality. What you are going to examine Use all of the gains of Apache Pig combine Apache Pig with different instruments expand Apache Pig Optimize Pig Latin code remedy varied use circumstances for Pig Latin Who This e-book Is For All degrees of IT pros: architects, titanic info fanatics, engineers, builders, and large information directors

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Here’s the syntax: Pig -e " " Although Pig Latin code is not easy to read, it is easy to modify. This command can also be embedded easily in the shell script for further automation. The following code uses the Pig –e command, and the semicolon represents the end of a statement: Pig -e "emp = load '/data/employee' using PigStorage(',');dump emp;" Pig -f The Pig –e command is not readable if it has multiple lines of code. Pig –f allows you to embed Pig Latin code in a file. pig extension to distinguish it from other script files.

It can contain values in the range of 2^31 to (2^31)–1, in other words, a minimum value of 2,147,483,648 and a maximum value of 2,147,483,647 (inclusive). The following shows some sample code that uses the int data type: Sales = load '/data/sales' as (eid:int); long The long data type is an 8-byte signed integer that is the same with respect to size and usage as in Java. It can contain values in the range of 2^63 to (2^63)–1. A lowercase l or an uppercase L is used to represent a long data type in Java.

It hides the key-value complexity of MapReduce from the programmer so that the programmer can focus on the business logic, unlike MapReduce. Cascading also has an API that provides several built-in analytics functions. You do not need to write functions such as count, max, and average, unlike MapReduce. It also provides an API for integration and scheduling apart from processing. Cascading is based on a metaphor called pipes and filters. Basically, Cascading allows you to define a pipeline that contains a list of pipes.

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