Download e-book for iPad: Beginning Apache Pig Big Data Processing Made Easy by Balaswamy Vaddeman

By Balaswamy Vaddeman

Learn how to use Apache Pig to enhance light-weight large info functions simply and fast. This ebook exhibits you several optimization thoughts and covers each context the place Pig is utilized in titanic information analytics. starting Apache Pig exhibits you the way Pig is straightforward to benefit and calls for particularly little time to advance mammoth facts functions. The publication is split into 4 elements: the total good points of Apache Pig integration with different instruments find out how to clear up complicated enterprise difficulties and optimization of instruments. Youll detect themes resembling MapReduce and why it can't meet each enterprise desire the positive aspects of Pig Latin corresponding to information forms for every load, shop, joins, teams, and ordering how Pig workflows may be created filing Pig jobs utilizing Hue and dealing with Oozie. Youll additionally see the right way to expand the framework via writing UDFs and customized load, shop, and filter out capabilities. ultimately youll conceal diverse optimization innovations reminiscent of collecting records a couple of Pig script, becoming a member of techniques, parallelism, and the position of knowledge codecs in sturdy functionality. What you'll 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 ebook Is For All degrees of IT execs: architects, monstrous info fans, engineers, builders, and massive info directors

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Table 2-3. Possible Castings Between Different Data Types From To int long float double chararray bytearray boolean int NA Yes Yes Yes Yes No No long Yes NA Yes Yes Yes No No float Yes Yes NA Yes Yes NO No double Yes Yes Yes NA Yes No No chararray Yes Yes Yes Yes NA No Yes bytearray Yes Yes Yes Yes Yes NA Yes boolean No No No No Yes No NA Comparison Operators The operators in Table 2-4 are used in Pig Latin to perform comparison operations such as equal, not equal, greater than, and so on. 29 Chapter 2 ■ Data Types Table 2-4.

You can create a new table from this table by prepending the create table as select statement like below. Create table wordcount as Benefits Hive is a scalable data warehousing system. Building a Hive team is easy because of its SQL interface. Unlike MapReduce, it is suitable for ad hoc querying. With many BI tools available on top of Hive, people without much programming experience can get insights from big data. It can easily be extensible using user-defined functions (UDFs). You can easily optimize code and also support several data formats such as text, sequence, RC, and ORC.

14 Chapter 1 ■ MapReduce and Its Abstractions LocalFlowConnector will help you to create a local flow that can be run on the local file system. You can use HadoopFlowConnector for creating a flow that works on the Hadoop file system. complete() will start executing the flow. 1. Modify the previous Cascading program to filter the word pear. Benefits These are the benefits of Cascading: • Like MapReduce, it can process all types of data, such as structured, semistructured, and unstructured data.

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