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Data Just Right: Introduction to Large-Scale Data & Analytics, CourseSmart eTextbook

By Michael Manoochehri

Published by Addison-Wesley Professional

Published Date: Dec 9, 2013

Description

Data Just Right -- a book utterly invaluable to every Big Data decision-maker, implementer, and strategist. Google's Michael Manoochehri organizes this book around today's key Big Data use cases, showing how they can be best addressed by combining technologies in hybrid solutions. Drawing on his own extensive experience, Manoochehri presents the technical detail needed to implement each solution, and best practices the reader can apply to any Big Data project.

Table of Contents

Foreword xv
Preface xvii
Acknowledgments xxv
About the Author xxvii

 

Part I: Directives in the Big Data Era 1

 

Chapter 1: Four Rules for Data Success 3

When Data Became a BIG Deal 3
Data and the Single Server 4
The Big Data Trade-Off 5
Anatomy of a Big Data Pipeline 9
The Ultimate Database 10
Summary 10

 

Part II: Collecting and Sharing a Lot of Data 11

 

Chapter 2: Hosting and Sharing Terabytes of Raw Data 13

Suffering from Files 14
Storage: Infrastructure as a Service 15
Choosing the Right Data Format 16
Character Encoding 19
Data in Motion: Data Serialization Formats 21
Summary 23

 

Chapter 3: Building a NoSQL-Based Web App to Collect Crowd-Sourced Data 25
Relational Databases: Command and Control 25
Relational Databases versus the Internet 28
Nonrelational Database Models 31
Leaning toward Write Performance: Redis 35
Sharding across Many Redis Instances 38
NewSQL: The Return of Codd 41
Summary 42

 

Chapter 4: Strategies for Dealing with Data Silos 43
A Warehouse Full of Jargon 43
Hadoop: The Elephant in the Warehouse 48
Data Silos Can Be Good 49
Convergence: The End of the Data Silo 51
Summary 53

 

Part III: Asking Questions about Your Data 55

 

Chapter 5: Using Hadoop, Hive, and Shark to Ask Questions about Large Datasets 57

What Is a Data Warehouse? 57
Apache Hive: Interactive Querying for Hadoop 60
Shark: Queries at the Speed of RAM 65
Data Warehousing in the Cloud 66
Summary 67

 

Chapter 6: Building a Data Dashboard with Google BigQuery 69
Analytical Databases 69
Dremel: Spreading the Wealth 71
BigQuery: Data Analytics as a Service 73
Building a Custom Big Data Dashboard 75
The Future of Analytical Query Engines 82
Summary 83

 

Chapter 7: Visualization Strategies for Exploring Large Datasets 85
Cautionary Tales: Translating Data into Narrative 86
Human Scale versus Machine Scale 89
Building Applications for Data Interactivity 90
Summary 96

 

Part IV: Building Data Pipelines 97

 

Chapter 8: Putting It Together: MapReduce Data Pipelines 99

What Is a Data Pipeline? 99
Data Pipelines with Hadoop Streaming 101
A One-Step MapReduce Transformation 105
Managing Complexity: Python MapReduce Frameworks for Hadoop 110
Summary 114

 

Chapter 9: Building Data Transformation Workflows with Pig and Cascading 117
Large-Scale Data Workflows in Practice 118
It’s Complicated: Multistep MapReduce
Transformations 118
Cascading: Building Robust Data-Workflow Applications 122
When to Choose Pig versus Cascading 128
Summary 128

 

Part V: Machine Learning for Large Datasets 129

 

Chapter 10: Building a Data Classification System with Mahout 131

Can Machines Predict the Future? 132
Challenges of Machine Learning 132
Apache Mahout: Scalable Machine Learning 136
MLBase: Distributed Machine Learning
Framework 139
Summary 140

 

Part VI: Statistical Analysis for Massive Datasets 143

 

Chapter 11: Using R with Large Datasets 145

Why Statistics Are Sexy 146
Strategies for Dealing with Large Datasets 149
Summary 155

 

Chapter 12: Building Analytics Workflows Using Python and Pandas 157
The Snakes Are Loose in the Data Zoo 157
Python Libraries for Data Processing 160
Building More Complex Workflows 167
iPython: Completing the Scientific Computing Tool Chain 170
Summary 174

 

Part VII: Looking Ahead 177

 

Chapter 13: When to Build, When to Buy, When to Outsource 179

Overlapping Solutions 179
Understanding Your Data Problem 181
A Playbook for the Build versus Buy Problem 182
My Own Private Data Center 184
Understand the Costs of Open-Source 186
Everything as a Service 187
Summary 187

 

Chapter 14: The Future: Trends in Data Technology 189
Hadoop: The Disruptor and the Disrupted 190
Everything in the Cloud 191
The Rise and Fall of the Data Scientist 193
Convergence: The Ultimate Database 195
Convergence of Cultures 196
Summary 197

 

Index 199

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Data Just Right: Introduction to Large-Scale Data & Analytics, CourseSmart eTextbook
Format: Safari Book

$19.99 | ISBN-13: 978-0-13-380554-3