Data Just Right: Introduction to Large Scale Data & Analytics

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

Michael Manoochehri is an entrepreneur, writer, and optimist. With many years of experience working with enterprise, research, and non-profit organizations, his goal is to help make scalable data analytics more affordable and accessible. Michael has been a member of Google's Cloud Platform developer relations team, focusing on cloud computing and data developer products such as Google BigQuery. In addition, Michael has written for the tech blog ProgrammableWeb.com, has spent time in rural Uganda researching mobile phone use, and holds a master's degree in information management and systems from UC Berkeley's School of Information.