This book is an easy-to-understand, practical guide to designing, testing, and implementing complex MapReduce applications in Scala using the Scalding framework. It is packed with examples featuring log-processing, ad-targeting, and machine learning. This book is for developers who are willing to discover how to effectively develop MapReduce applications. Prior knowledge of Hadoop or Scala is not required; however, investing some time on those topics would certainly be beneficial.

What you will learn

Set up an environment to execute jobs in local and Hadoop mode
Preview the complete Scalding API through examples and illustrations
Learn about Scalding capabilities, testing, and pipelining jobs
Understand the concepts of MapReduce patterns and the applications of its ecosystem
Implement logfile analysis and adtargeting applications using best practices
Apply a testdriven development (TDD) methodology and structure Scalding applications in a modular and testable way
Interact with external NoSQL and SQL data stores from Scalding
Deploy, schedule, monitor, and maintain production systems

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Tushar Kapila Oct 28, 2015

few more examples with mixed join, how to refer to the elements in a fold, would be usful for everyday work

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tom Oct 22, 2015

It's got good coverage and examples to get you descent with fields API, but missing a bit on explaining how the code would translate into mappers and reducers and what you should watch for to optimize your code. It's also out dated since people are using the typed API now.

Artie Apr 22, 2015

This book does a good job of explaining scalding's fields based API but only has two paragraphs on the type safe API. It is clear that the type safe API is the better approach to writing scalding jobs so lacking this is a serious omission in my opinion. I do hope that the author and publisher can publish an update covering this feature.

soulmachine Feb 23, 2015

This book is very easy to understand because it has many tiny examples that are very detailed to let you understand core APIs quickly.Chapter 4 "Intermediate Examples" elaborate two complete examples.Chapter 5 "Scalding Design Patterns" introduces three kind of desing paterns that are very pratical and insightful.The only weakness of this book is that it uses fields based APIs, but in my opinion, type safe APIs are more modern and elegant, however, all the knowlege of fiedls based APIs can apply to type safe APIs seeminglessly.

Si Dunn Jul 29, 2014

Programming MapReduce with Scalding offers clear, well-illustrated, smoothly paced how-to steps, as well as easy-to-digest definitions and descriptions. It takes the reader from setting up and running a Hadoop mini-cluster and local-development environment to applying Scalding to real-use cases, as well as developing good test and test-driven development methodologies, running Scalding in production, using external data stores, and applying matrix calculations and machine learning.The book is written for developers who have at least "a basic understanding" of Hadoop and MapReduce, but is also intended for experienced Hadoop developers who may be "enlightened by this alternative methodology of developing MapReduce applications with Scalding."It does help to be somewhat familiar with MapReduce, Scalding, Scala, Hadoop, Maven, Eclipse and the Linux environment. But Antonio Chalkiopoulo does a good job of keeping the examples accessible even when readers are new to some of the packages.

Programming MapReduce with Scalding: A practical guide to designing, testing, and implementing complex MapReduce applications in Scala

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