By Balaswamy Vaddeman
Learn how to use Apache Pig to increase light-weight enormous information functions simply and speedy. This publication indicates you several optimization strategies and covers each context the place Pig is utilized in huge information analytics. starting Apache Pig exhibits you the way Pig is simple to profit and calls for quite little time to advance great information functions. The e-book is split into 4 elements: the whole beneficial properties of Apache Pig integration with different instruments find out how to clear up advanced company difficulties and optimization of instruments. Youll become aware of subject matters corresponding to MapReduce and why it can't meet each company want the beneficial properties of Pig Latin reminiscent of information forms for every load, shop, joins, teams, and ordering how Pig workflows could be created filing Pig jobs utilizing Hue and dealing with Oozie. Youll additionally see tips on how to expand the framework via writing UDFs and customized load, shop, and filter out capabilities. eventually youll disguise various optimization concepts reminiscent of accumulating facts a couple of Pig script, becoming a member of ideas, parallelism, and the position of knowledge codecs in sturdy functionality. What you are going to examine Use the entire gains of Apache Pig combine Apache Pig with different instruments expand Apache Pig Optimize Pig Latin code remedy assorted use instances for Pig Latin Who This publication Is For All degrees of IT execs: architects, significant information lovers, engineers, builders, and large information directors
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Extra resources for Beginning Apache Pig Big Data Processing Made Easy
Apache Pig provides two data types. They are simple and complex, as specified in Figure 2-1. The simple data types include int, long, float, double, boolean, chararray, bytearray, datetime, biginteger, and bigdecimal. The complex data types are map, tuple, and bag. Figure 2-1. Data types in Pig © Balaswamy Vaddeman 2016 B. 1007/978-1-4842-2337-6_2 21 Chapter 2 ■ Data Types In Pig Latin, data types are specified as part of the schema after the as keyword and within brackets. The field name is specified first and then the data type.
It supports many data formats such as text, sequence, ORC, and Parquet. Pigs Live Anywhere Apache Pig is big data–processing tool that was first implemented on Apache Hadoop. It can even process local file system data. Pigs Are Domestic Animals Like other domestic animals, pigs are friendly animals, and Apache Pig is user friendly. Apache Pig is easy to use and simple to learn. If a schema not specified, it takes the default schema. It applies the default load and store functions if not specified and applies the default delimiter if not given by the user.
Write the command to compile the Java program. java file by compiling it with the javac program. jar in the classpath so that the Pig API in the Java program is resolved. The following command compiles Java file. jar StoreEmp. java. 3. Write the command to run the Java program. 0-3485/pig/lib/*:. csv dumpempout If Pig cannot find its dependent JARs, the Java program might fail and throw a “class not found” exception. To avoid such exceptions, include all the required JARs in the class path using the -cp option.