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Practical Data Science with Hadoop and Spark: Designing and Building Effective Analytics at Scale

Practical Data Science with Hadoop and Spark: Designing and Building Effective Analytics at Scale

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  • Estimated Release: Dec 16, 2016
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Description

  • Copyright 2017
  • Dimensions: 7" x 9-1/8"
  • Pages: 256
  • Edition: 1st
  • eBook (Watermarked)
  • ISBN-10: 0-13-402974-7
  • ISBN-13: 978-0-13-402974-0

The Complete Guide to Data Science with Hadoop—For Technical Professionals, Businesspeople, and Students

Demand is soaring for professionals who can solve real data science problems with Hadoop and Spark. Practical Data Science with Hadoop® and Spark is your complete guide to doing just that. Drawing on immense experience with Hadoop and big data, three leading experts bring together everything you need: high-level concepts, deep-dive techniques, real-world use cases, practical applications, and hands-on tutorials.

The authors introduce the essentials of data science and the modern Hadoop ecosystem, explaining how Hadoop and Spark have evolved into an effective platform for solving data science problems at scale. In addition to comprehensive application coverage, the authors also provide useful guidance on the important steps of data ingestion, data munging, and visualization.

Once the groundwork is in place, the authors focus on specific applications, including machine learning, predictive modeling for sentiment analysis, clustering for document analysis, anomaly detection, and natural language processing (NLP).

This guide provides a strong technical foundation for those who want to do practical data science, and also presents business-driven guidance on how to apply Hadoop and Spark to optimize ROI of data science initiatives.

Learn

  • What data science is, how it has evolved, and how to plan a data science career
  • How data volume, variety, and velocity shape data science use cases
  • Hadoop and its ecosystem, including HDFS, MapReduce, YARN, and Spark
  • Data importation with Hive and Spark
  • Data quality, preprocessing, preparation, and modeling
  • Visualization: surfacing insights from huge data sets
  • Machine learning: classification, regression, clustering, and anomaly detection
  • Algorithms and Hadoop tools for predictive modeling
  • Cluster analysis and similarity functions
  • Large-scale anomaly detection
  • NLP: applying data science to human language

Sample Content

Table of Contents

Foreword xiii

Preface xv

Acknowledgments xxi

About the Authors xxiii

Part I: Data Science with Hadoop—An Overview 1

Chapter 1: Introduction to Data Science 3

What Is Data Science? 3

Example: Search Advertising 4

A Bit of Data Science History 5

Becoming a Data Scientist 8

Building a Data Science Team 12

The Data Science Project Life Cycle 13

Managing a Data Science Project 18

Summary 18

Chapter 2: Use Cases for Data Science 19

Big Data—A Driver of Change 19

Business Use Cases 21

Summary 29

Chapter 3: Hadoop and Data Science 31

What Is Hadoop? 31

Hadoop’s Evolution 37

Hadoop Tools for Data Science 38

Why Hadoop Is Useful to Data Scientists 46

Summary 51

Part II: Preparing and Visualizing Data with Hadoop 53

Chapter 4: Getting Data into Hadoop 55

Hadoop as a Data Lake 56

The Hadoop Distributed File System (HDFS) 58

Direct File Transfer to Hadoop HDFS 58

Importing Data from Files into Hive Tables 59

Importing Data into Hive Tables Using Spark 62

Using Apache Sqoop to Acquire Relational Data 65

Using Apache Flume to Acquire Data Streams 74

Manage Hadoop Work and Data Flows with Apache

Oozie 79

Apache Falcon 81

What’s Next in Data Ingestion? 82

Summary 82

Chapter 5: Data Munging with Hadoop 85

Why Hadoop for Data Munging? 86

Data Quality 86

The Feature Matrix 93

Summary 106

Chapter 6: Exploring and Visualizing Data 107

Why Visualize Data? 107

Creating Visualizations 112

Using Visualization for Data Science 121

Popular Visualization Tools 121

Visualizing Big Data with Hadoop 123

Summary 124

Part III: Applying Data Modeling with Hadoop 125

Chapter 7: Machine Learning with Hadoop 127

Overview of Machine Learning 127

Terminology 128

Task Types in Machine Learning 129

Big Data and Machine Learning 130

Tools for Machine Learning 131

The Future of Machine Learning and Artificial Intelligence 132

Summary 132

Chapter 8: Predictive Modeling 133

Overview of Predictive Modeling 133

Classification Versus Regression 134

Evaluating Predictive Models 136

Supervised Learning Algorithms 140

Building Big Data Predictive Model Solutions 141

Example: Sentiment Analysis 145

Summary 150

Chapter 9: Clustering 151

Overview of Clustering 151

Uses of Clustering 152

Designing a Similarity Measure 153

Clustering Algorithms 154

Example: Clustering Algorithms 155

Evaluating the Clusters and Choosing the Number of Clusters 157

Building Big Data Clustering Solutions 158

Example: Topic Modeling with Latent Dirichlet Allocation 160

Summary 163

Chapter 10: Anomaly Detection with Hadoop 165

Overview 165

Uses of Anomaly Detection 166

Types of Anomalies in Data 166

Approaches to Anomaly Detection 167

Tuning Anomaly Detection Systems 170

Building a Big Data Anomaly Detection Solution with Hadoop 171

Example: Detecting Network Intrusions 172

Summary 179

Chapter 11: Natural Language Processing 181

Natural Language Processing 181

Tooling for NLP in Hadoop 184

Textual Representations 187

Sentiment Analysis Example 189

Summary 193

Chapter 12: Data Science with Hadoop—The Next Frontier 195

Automated Data Discovery 195

Deep Learning 197

Summary 199

Appendix A: Book Web Page and Code Download 201

Appendix B: HDFS Quick Start 203

Quick Command Dereference 204

Appendix C: Additional Background on Data Science and Apache Hadoop and Spark 209

General Hadoop/Spark Information 209

Hadoop/Spark Installation Recipes 210

HDFS 210

MapReduce 211

Spark 211

Essential Tools 211

Machine Learning 212

Index 213

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