Über den Autor
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.
There is an easier way to build Hadoop applications. With this hands-on book, you’ll 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, you’ll quickly learn Cascading’s 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.
* Start working on Cascading example projects right away
* Model and analyze unstructured data in any format, from any source
* Build and test applications with familiar constructs and reusable components
* Work with the Scalding and Cascalog Domain-Specific Languages
* Easily deploy applications to Hadoop, regardless of cluster location or data size
* Build workflows that integrate several big data frameworks and processes
* Explore common use cases for Cascading, including features and tools that support them
* Examine a case study that uses a dataset from the Open Data Initiative