Practical Data Science with Hadoop and Spark
Designing and Building Effective Analytics at Scale
1st Edition
Published on 8. December 2016
256 pages
978-0-13-402971-9 (ISBN)
Description
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 (R) 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
Demand is soaring for professionals who can solve real data science problems with Hadoop and Spark. Practical Data Science with Hadoop (R) 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
All prices
More details
Edition
1. Auflage
Language
English
Place of publication
Boston
United States
Publishing group
Pearson Education (US)
Target group
Professional and scholarly
File size
3,09 MB
ISBN-13
978-0-13-402971-9 (9780134029719)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Ofer Mendelevitch | Casey Stella | Douglas Eadline
Practical Data Science with Hadoop and Spark: Designing and Building Effective Analytics at Scale
Designing and Building Effective Analytics at Scale
Book
02/2017
1st Edition
Addison Wesley
€41.49
Shipment within 15-20 days
Persons
Ofer Mendelevitch is Vice President of Data Science at Lendup, where he is responsible for Lendup's machine learning and advanced analytics group. Prior to joining Lendup, Ofer was Director of Data Science at Hortonworks, where he was responsible for helping Hortonwork's customers apply Data Science with Hadoop and Spark to big data across various industries including healthcare, finance, retail and others. Before Hortonworks, Ofer served as Entrepreneur in Residence at XSeed Capital, VP of Engineering at Nor1, and Director of Engineering at Yahoo!.
Casey Stella is a Principal Software Engineer focusing on Data Science at Hortonworks, which provides an open source Hadoop distribution. Casey's primary responsibility is leading the analytics/data science team for the Apache Metron (Incubating) Project, an open source cybersecurity project. Prior to Hortonworks, Casey was an architect at Explorys, which was a medical informatics startup spun out of the Cleveland Clinic. In the more distant past, Casey served as a developer at Oracle, Research Geophysicist at ION Geophysical and as a poor graduate student in Mathematics at Texas A&M.
Douglas Eadline, PhD, began his career as analytical chemist with an interest in computer methods. Starting with the first Beowulf how-to document, Doug has written hundreds of articles, white papers, and instructional documents covering many aspects of HPC and Hadoop computing. Prior to starting and editing the popular ClusterMonkey.net website in 2005, he served as editor?in?chief for ClusterWorld Magazine and was senior HPC editor for Linux Magazine. He has practical hands-on experience in many aspects of HPC and Apache Hadoop, including hardware and software design, benchmarking, storage, GPU, cloud computing, and parallel computing. Currently, he is a writer and consultant to the HPC/analytics industry and leader of the Limulus Personal Cluster Project (http://limulus.basement-supercomputing.com). He is author of the Apache Hadoop (R) Fundamentals LiveLessons and Apache Hadoop (R) YARN Fundamentals LiveLessons videos from Pearson, and is book co-author of Apache Hadoop (R) YARN: Moving beyond MapReduce and Batch Processing with Apache Hadoop 2 and author of Hadoop (R) 2 Quick Start Guide: Learn the Essentials of Big Data Computing in the Apache Hadoop 2 Ecosystem, also from Addison-Wesley, and is author of High Performance Computing for Dummies.
Casey Stella is a Principal Software Engineer focusing on Data Science at Hortonworks, which provides an open source Hadoop distribution. Casey's primary responsibility is leading the analytics/data science team for the Apache Metron (Incubating) Project, an open source cybersecurity project. Prior to Hortonworks, Casey was an architect at Explorys, which was a medical informatics startup spun out of the Cleveland Clinic. In the more distant past, Casey served as a developer at Oracle, Research Geophysicist at ION Geophysical and as a poor graduate student in Mathematics at Texas A&M.
Douglas Eadline, PhD, began his career as analytical chemist with an interest in computer methods. Starting with the first Beowulf how-to document, Doug has written hundreds of articles, white papers, and instructional documents covering many aspects of HPC and Hadoop computing. Prior to starting and editing the popular ClusterMonkey.net website in 2005, he served as editor?in?chief for ClusterWorld Magazine and was senior HPC editor for Linux Magazine. He has practical hands-on experience in many aspects of HPC and Apache Hadoop, including hardware and software design, benchmarking, storage, GPU, cloud computing, and parallel computing. Currently, he is a writer and consultant to the HPC/analytics industry and leader of the Limulus Personal Cluster Project (http://limulus.basement-supercomputing.com). He is author of the Apache Hadoop (R) Fundamentals LiveLessons and Apache Hadoop (R) YARN Fundamentals LiveLessons videos from Pearson, and is book co-author of Apache Hadoop (R) YARN: Moving beyond MapReduce and Batch Processing with Apache Hadoop 2 and author of Hadoop (R) 2 Quick Start Guide: Learn the Essentials of Big Data Computing in the Apache Hadoop 2 Ecosystem, also from Addison-Wesley, and is author of High Performance Computing for Dummies.
Content
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
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
