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Synopsis
This guide is an ideal learning tool and reference for Apache Pig, the open source engine for executing parallel data flows on Hadoop. With Pig, you can batch-process data without having to create a full-fledged application―making it easy for you to experiment with new datasets.
Programming Pig introduces new users to Pig, and provides experienced users with comprehensive coverage on key features such as the Pig Latin scripting language, the Grunt shell, and User Defined Functions (UDFs) for extending Pig. If you need to analyze terabytes of data, this book shows you how to do it efficiently with Pig.
- Delve into Pig’s data model, including scalar and complex data types
- Write Pig Latin scripts to sort, group, join, project, and filter your data
- Use Grunt to work with the Hadoop Distributed File System (HDFS)
- Build complex data processing pipelines with Pig’s macros and modularity features
- Embed Pig Latin in Python for iterative processing and other advanced tasks
- Create your own load and store functions to handle data formats and storage mechanisms
- Get performance tips for running scripts on Hadoop clusters in less time
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About the Author
Alan is an original member of the engineering team that took Pig from a Yahoo! Labs research project to a successful Apache open source project. In this role he oversaw the implementation of the language, including programming interfaces and the overall design. He has presented Pig at numerous conferences and user groups, universities, and companies. Alan is a member of the Apache Software Foundation and a co-founder of Hortonworks. He has a BS in Mathematics from Oregon State University and a MA in Theology from Fuller Theological Seminary.
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