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Data Munging with Hadoop

By Ofer Mendelevitch, Casey Stella
Published Nov 21, 2015 by Addison-Wesley Professional.

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Description

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Description

Copyright 2016
Dimensions: 7" x 9-1/8"
Edition: 1st

eBook (Watermarked)
ISBN-10: 0-13-443550-8
ISBN-13: 978-0-13-443550-3

The Example-Rich, Hands-On Guide to Data Munging with Apache Hadoop^TM

Data scientists spend much of their time “munging” data: handling day-to-day tasks such as data cleansing, normalization, aggregation, sampling, and transformation. These tasks are both critical and surprisingly interesting. Most important, they deepen your understanding of your data’s structure and limitations: crucial insight for improving accuracy and mitigating risk in any analytical project.

Now, two leading Hortonworks data scientists, Ofer Mendelevitch and Casey Stella, bring together powerful, practical insights for effective Hadoop-based data munging of large datasets. Drawing on extensive experience with advanced analytics, the authors offer realistic examples that address the common issues you’re most likely to face. They describe each task in detail, presenting example code based on widely used tools such as Pig, Hive, and Spark.

This concise, hands-on eBook is valuable for every data scientist, data engineer, and architect who wants to master data munging: not just in theory, but in practice with the field’s #1 platform–Hadoop.

Coverage includes

A framework for understanding the various types of data quality checks, including cell-based rules, distribution validation, and outlier analysis
Assessing tradeoffs in common approaches to imputing missing values
Implementing quality checks with Pig or Hive UDFs
Transforming raw data into “feature matrix” format for machine learning algorithms
Choosing features and instances
Implementing text features via “bag-of-words” and NLP techniques
Handling time-series data via frequency- or time-domain methods
Manipulating feature values to prepare for modeling

Data Munging with Hadoop is part of a larger, forthcoming work entitled Data Science Using Hadoop. To be notified when the larger work is available, register your purchase of Data Munging with Hadoop at informit.com/register and check the box “I would like to hear from InformIT and its family of brands about products and special offers.”

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Sample Content

Preface vi

About the Authors viii

Data Munging with Hadoop 1

Why Hadoop for Data Munging? 2

Data Quality 2

What Is Data Quality? 2

Dealing with Data Quality Issues 3

Using Hadoop for Data Quality 8

The Feature Matrix 9

Choosing the “Right” Features 10

Sampling: Choosing Instances 10

Generating Features 12

Text Features 13

Time-Series Features 16

Features from Complex Data Types 17

Feature Manipulation 18

Dimensionality Reduction 19

Summary 22

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