Practical Data Science with Hadoop and Spark

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