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About this Book

Doug Cutting, the creator of Hadoop, likes to call Hadoop the kernel for big data, and I would tend to agree. With its distributed storage and compute capabilities, Hadoop is fundamentally an enabling technology for working with huge datasets. Hadoop provides a bridge between structured (RDBMS) and unstructured (log files, XML, text) data and allows these datasets to be easily joined together. This has evolved from traditional use cases, such as combining OLTP and log files, to more sophisticated uses, such as using Hadoop for data warehousing (exemplified by Facebook) and the field of data science, which studies and makes new discoveries about data.

This book collects a number of intermediary and advanced Hadoop examples and presents them in a problem/solution format. Each technique addresses a specific task you’ll face, like using Flume to move log files into Hadoop or using Mahout for predictive analysis. Each problem is explored step by step, and as you work through them, you’ll find yourself growing more comfortable with Hadoop and at home in the world of big data.

This hands-on book targets users who have some practical experience with Hadoop and understand the basic concepts of MapReduce and HDFS. Manning’s Hadoop in Action by Chuck Lam contains the necessary prerequisites to understand and apply the techniques covered in this book.

Roadmap

What’s new in the second edition?

Getting help

Code conventions and downloads

Third-party libraries

Datasets

NASDAQ financial stocks

Apache log data

Names

Author Online

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