Enterprise Data Workflows with Cascading

There is an easier way to build Hadoop applications. With this hands-on book, you{u2019}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{u2014}without having to learn the intricacies of MapReduce. Working with sample apps based on Java and other JVM languages, you{u2019}ll quickly learn Cascading{u2019}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 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 you're a developer, data scientist, or system/IT administrator, you'll quickly learn Cascading's 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. Examine best practices for using data science in enterprise-scale apps Learn how to use workflows that reach beyond MapReduce to integrate... COMPUTERS / Data Processing