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Google Bigquery Analytics

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Table of Contents

Introduction xiii Part I BigQuery Fundamentals Chapter 1 The Story of Big Data at Google 3 Big Data Stack 1.0 4 Big Data Stack 2.0 (and Beyond) 5 Open Source Stack 7 Google Cloud Platform 8 Cloud Processing 9 Cloud Storage 9 Cloud Analytics 9 Problem Statement 10 What Is Big Data? 10 Why Big Data? 10 Why Do You Need New Ways to Process Big Data? 11 How Can You Read a Terabyte in a Second? 12 What about MapReduce? 12 How Can You Ask Questions of Your Big Data and Quickly Get Answers? 13 Summary 13 Chapter 2 BigQuery Fundamentals 15 What Is BigQuery? 15 SQL Queries over Big Data 16 Cloud Storage System 21 Distributed Cloud Computing 23 Analytics as a Service (AaaS?) 26 What BigQuery Isn t 29 BigQuery Technology Stack 31 Google Cloud Platform 34 BigQuery Service History 37 BigQuery Sensors Application 39 Sensor Client Android App 40 BigQuery Sensors AppEngine App 41 Running Ad-Hoc Queries 42 Summary 43 Chapter 3 Getting Started with BigQuery 45 Creating a Project 45 Google APIs Console 46 Free Tier Limitations and Billing 49 Running Your First Query 51 Loading Data 54 Using the Command-Line Client 57 Install and Setup 58 Using the Client 60 Service Account Access 62 Setting Up Google Cloud Storage 64 Development Environment 66 Python Libraries 66 Java Libraries 67 Additional Tools 67 Summary 68 Chapter 4 Understanding the BigQuery Object Model 69 Projects 70 Project Names 70 Project Billing 72 Project Access Control 72 Projects and AppEngine 73 BigQuery Data 73 Naming in BigQuery 73 Schemas 75 Tables 76 Datasets 77 Jobs 78 Job Components 78 BigQuery Billing and Quotas 85 Storage Costs 85 Processing Costs 86 Query RPCs 87 TableData.insertAll() RPCs 87 Data Model for End-to-End Application 87 Project 87 Datasets 88 Tables 89 Summary 91 Part II Basic BigQuery 93 Chapter 5 Talking to the BigQuery API 95 Introduction to Google APIs 95 Authenticating API Access 96 RESTful Web Services for the SOAP-Less Masses 105 Discovering Google APIs 112 Common Operations 113 BigQuery REST Collections 122 Projects 123 Datasets 126 Tables 132 TableData 139 Jobs 144 BigQuery API Tour 151 Error Handling in BigQuery 154 Summary 158 Chapter 6 Loading Data 159 Bulk Loads 160 Moving Bytes 163 Destination Table 170 Data Formats 174 Errors 182 Limits and Quotas 186 Streaming Inserts 188 Summary 193 Chapter 7 Running Queries 195 BigQuery Query API 196 Query API Methods 196 Query API Features 208 Query Billing and Quotas 213 BigQuery Query Language 221 BigQuery SQL in Five Queries 222 Differences from Standard SQL 232 Summary 236 Chapter 8 Putting It Together 237 A Quick Tour 238 Mobile Client 242 Monitoring Service 243 Log Collection Service 252 Log Trampoline 253 Dashboard 260 Data Caching 261 Data Transformation 265 Web Client 269 Summary 272 Part III Advanced BigQuery 273 Chapter 9 Understanding Query Execution 275 Background 276 Storage Architecture 277 Colossus File System (CFS) 277 ColumnIO 278 Durability and Availability 281 Query Processing 282 Dremel Serving Trees 283 Architecture Comparisons 295 Relational Databases 295 MapReduce 298 Summary 303 Chapter 10 Advanced Queries 305 Advanced SQL 306 Subqueries 307 Combining Tables: Implicit UNION and JOIN 310 Analytic and Windowing Functions 315 BigQuery SQL Extensions 318 The EACH Keyword 318 Data Sampling 320 Repeated Fields 324 Query Errors 334 Result Too Large 334 Resources Exceeded 337 Recipes 338 Pivot 339 Cohort Analysis 340 Parallel Lists 343 Exact Count Distinct 344 Trailing Averages 346 Finding Concurrency 347 Summary 348 Chapter 11 Managing Data Stored in BigQuery 349 Query Caching 349 Result Caching 350 Table Snapshots 354 AppEngine Datastore Integration 358 Simple Kind 359 Mixing Types 366 Final Thoughts 368 Metatables and Table Sharding 368 Time Travel 368 Selecting Tables 374 Summary 378 Part IV BigQuery Applications 381 Chapter 12 External Data Processing 383 Getting Data Out of BigQuery 384 Extract Jobs 384 TableData.list() 396 AppEngine MapReduce 405 Sequential Solution 407 Basic AppEngine MapReduce 409 BigQuery Integration 412 Using BigQuery with Hadoop 418 Querying BigQuery from a Spreadsheet 419 BigQuery Queries in Google Spreadsheets (Apps Script) 419 BigQuery Queries in Microsoft Excel 429 Summary 433 Chapter 13 Using BigQuery from Third-Party Tools 435 BigQuery Adapters 436 Simba ODBC Connector 436 JDBC Connection Options 444 Client-Side Encryption with Encrypted BigQuery 445 Scientifi c Data Processing Tools in BigQuery 452 BigQuery from R 452 Python Pandas and BigQuery 461 Visualizing Data in BigQuery 467 Visualizing Your BigQuery Data with Tableau 467 Visualizing Your BigQuery Data with BIME 473 Other Data Visualization Options 477 Summary 478 Chapter 14 Querying Google Data Sources 479 Google Analytics 480 Setting Up BigQuery Access 480 Table Schema 481 Querying the Tables 483 Google AdSense 485 Table Structure 486 Leveraging BigQuery 490 Google Cloud Storage 491 Summary 494 Index 495

About the Author

The authors are founding members of the BigQuery team and have helped build and run the service. Jordan Tigani is an active participant in the BigQuery StackOverflow virtual community. Siddartha Naidu has extensive experience helping customers integrate with BigQuery.


"If you're at all interested in Google BigQuery, this is an excellent book. The descriptions and sample code are clear and easy to understand, and the fact the authors are so involved with the project means they include insights into why things were designed in that particular way. There are useful descriptions of the differences between BigQuery and other tools such as MapReduce, and overall you'll come out with a much clearer view of the big data scene right now, and how everything fits together." (I Programmer, August 2014)

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