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Enterprise Data Workflows with Cascadingby Paco Nathan
Synopses & Reviews
Despite its growing use in the enterprise, building applications for Hadoop is notoriously difficult. But there is a solution. This hands-on book introduces you to Cascading, the framework that enables you to build powerful data processing applications on Hadoop without having to spend months learning the intricacies of MapReduce.
Whether youre a developer, data scientist, or system/IT administrator, youll quickly learn Cascadings streamlined approach to data processing, data filtering, and workflow optimization, using sample apps based on Java, Scala, and Clojure. Companies such as Etsy, Razorfish, TeleNav, and Twitter already use Cascading for mission-critical applications. This book shows you how this framework can help your organization extract meaningful information from large amounts of distributed data.
There is an easier way to build Hadoop applications. With this hands-on book, youll learn how to use Cascading, the open source abstraction framework for Hadoop that lets you easily create and manage powerful enterprise-grade data processing applications—without having to learn the intricacies of MapReduce.
Working with sample apps based on Java and other JVM languages, youll quickly learn Cascadings streamlined approach to data processing, data filtering, and workflow optimization. This book demonstrates how this framework can help your business extract meaningful information from large amounts of distributed data.
About the Author
Paco Nathan is a Data Scientist at Concurrent, Inc., and heads up the developer outreach program there. He has a dual background from Stanford in math/stats and distributed computing, with 25+ years experience in the tech industry. As an expert in Hadoop, R, predictive analytics, machine learning, natural language processing, Paco has built and led several expert Data Science teams, with data infrastructure based on large-scale cloud deployments. He has presented twice on the AWS Start-Up Tour, and gives talks often about Hadoop, Data Science, and Cloud Computing.
Table of Contents
PrefaceChapter 1: Getting StartedChapter 2: Extending Pipe AssembliesChapter 3: Test-Driven DevelopmentChapter 4: Scalding—A Scala DSL for CascadingChapter 5: Cascalog—A Clojure DSL for CascadingChapter 6: Beyond MapReduceChapter 7: The Workflow AbstractionChapter 8: Case Study: City of Palo Alto Open DataTroubleshooting WorkflowsIndexColophon
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