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  • Book Overview & Buying Programming MapReduce with Scalding
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Programming MapReduce with Scalding

Programming MapReduce with Scalding

By : Antonios Chalkiopoulos
4.3 (6)
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Programming MapReduce with Scalding

Programming MapReduce with Scalding

4.3 (6)
By: Antonios Chalkiopoulos

Overview of this book

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.
Table of Contents (11 chapters)
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10
Index

Other libraries

For mining massive datasets, we can utilize the Algebird abstract algebra library for Scala, also open sourced by Twitter. The code was originally developed as part of the Scalding Matrix API. As it had broader applications in aggregation systems, such as Scalding and Storm, it became a separate library.

Locality Sensitivity Hashing is a technique that minimizes the data space and can provide an approximate similarity. It is based on the idea that items that have high-dimensional properties can be hashed into a smaller space but still produce results with high accuracy.

An implementation of the approximate Jaccard item-similarity using Locality Sensitive Hashing (LSH) is provided in the source code accompanying this book.

Another interesting open source project that integrates Mahout vectors into Scalding and provides implementations of Naive Bayes classifiers and K-Means is Ganitha, which can be found at https://github.com/tresata/ganitha. This library, among others, simplifies...

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Programming MapReduce with Scalding
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