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For many organizations, Hadoop is the first step for dealing with massive amounts of data. The next step? Processing and analyzing datasets with the Apache Pig scripting platform. With Pig, you can batch-process data without having to create a full-fledged application, making it easy to experiment with new datasets.

Updated with use cases and programming examples, this second edition is the ideal learning tool for new and experienced users alike. You'll find comprehensive coverage on key features such as the Pig Latin scripting language and the Grunt shell. When 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
• Use Pig with Apache Tez to build high-performance batch and interactive data processing applications
• Create your own load and store functions to handle data formats and storage mechanisms

Daniel is an Apache Pig PMC member/committer involved with Pig for 6 years at Yahoo and now at Hortonworks. He has a PhD in Computer Science from University of Central Florida, with a specialization in distributed computing, data mining and computer security.

Alan is co-founder of Hortonworks and an original member of the engineering team that took Pig from a Yahoo! Labs research project to a successful Apache open source project. Alan also designed HCatalog and guided its adoption as an Apache Incubator project. Alan has a BS in Mathematics from Oregon State University and a MA in Theology from Fuller Theological Seminary. He is also the author of Programming Pig, a book from O'Reilly Press. Follow Alan on Twitter: @alanfgates.