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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
Buy this Book

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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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Introduction to MapReduce
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1. Introduction to MapReduce
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The Hadoop platform
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MapReduce
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MapReduce abstractions
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Introducing Cascading
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Summary
2
2. Get Ready for Scalding
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2. Get Ready for Scalding
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Why Scala?
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Scala basics
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Scala build tools
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Hello World in Scala
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Development editors
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Installing Hadoop in five minutes
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Running our first Scalding job
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Submitting a Scalding job in Hadoop
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Summary
3
3. Scalding by Example
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3. Scalding by Example
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Reading and writing files
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Understanding the core capabilities of Scalding
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Operations on groups
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A simple example
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Typed API
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Summary
4
4. Intermediate Examples
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4. Intermediate Examples
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Logfile analysis
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Exploring ad targeting
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Summary
5
5. Scalding Design Patterns
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5. Scalding Design Patterns
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The external operations pattern
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The dependency injection pattern
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The late bound dependency pattern
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Summary
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6. Testing and TDD
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6. Testing and TDD
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Introduction to testing
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MapReduce testing challenges
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Development lifecycle with testing strategy
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TDD for Scalding developers
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Black box testing
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Summary
7
7. Running Scalding in Production
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7. Running Scalding in Production
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Executing Scalding in a Hadoop cluster
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Scheduling execution
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Coordinating job execution
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Configuring using a property file
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Configuring using Hadoop parameters
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Monitoring Scalding jobs
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Using slim JAR files
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Scalding execution throttling
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Summary
8
8. Using External Data Stores
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8. Using External Data Stores
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Interacting with external systems
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SQL databases
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NoSQL databases
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Search platforms
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Summary
9
9. Matrix Calculations and Machine Learning
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9. Matrix Calculations and Machine Learning
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Text similarity using TF-IDF
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Setting a similarity using the Jaccard index
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K-Means using Mahout
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Other libraries
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Summary
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Index
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Index

Summary

Scalding provides a number of ways to implement and execute machine learning algorithms. As presented, we can manipulate pipes, use the Matrix API or algebird, and interoperate with existing libraries such as Mahout.

The majority of ML jobs originate as Big Data ETL jobs that reduce to a smaller data space. The final result usually needs some form of post-processing, and it is then stored in an external source. Scalding provides great interoperability with external systems, and it is thus one of the most suitable technologies to solve such problems.

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Tech Concepts
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Programming languages
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Programming MapReduce with Scalding
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