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Google BigQuery Analytics

Book Description

How to effectively use BigQuery, avoid common mistakes, and execute sophisticated queries against large datasets

Google BigQuery Analytics is the perfect guide for business and data analysts who want the latest tips on running complex queries and writing code to communicate with the BigQuery API. The book uses real-world examples to demonstrate current best practices and techniques, and also explains and demonstrates streaming ingestion, transformation via Hadoop in Google Compute engine, AppEngine datastore integration, and using GViz with Tableau to generate charts of query results. In addition to the mechanics of BigQuery, the book also covers the architecture of the underlying Dremel query engine, providing a thorough understanding that leads to better query results.

  • Features a companion website that includes all code and data sets from the book

  • Uses real-world examples to explain everything analysts need to know to effectively use BigQuery

  • Includes web application examples coded in Python

  • Table of Contents

    1. Part I: BigQuery Fundamentals
      1. In This Part
    2. Chapter 1: The Story of Big Data at Google
      1. Big Data Stack 1.0
      2. Big Data Stack 2.0 (and Beyond)
      3. Open Source Stack
      4. Google Cloud Platform
      5. Problem Statement
      6. Summary
    3. Chapter 2: BigQuery Fundamentals
      1. What Is BigQuery?
      2. BigQuery Sensors Application
      3. Summary
    4. Chapter 3: Getting Started with BigQuery
      1. Creating a Project
      2. Running Your First Query
      3. Using the Command-Line Client
      4. Setting Up Google Cloud Storage
      5. Development Environment
      6. Summary
    5. Chapter 4: Understanding the BigQuery Object Model
      1. Projects
      2. BigQuery Data
      3. Jobs
      4. BigQuery Billing and Quotas
      5. Data Model for End-to-End Application
      6. Summary
    6. Part II: Basic BigQuery
      1. In This Part
    7. Chapter 5: Talking to the BigQuery API
      1. Introduction to Google APIs
      2. BigQuery REST Collections
      3. Summary
    8. Chapter 6: Loading Data
      1. Bulk Loads
      2. Streaming Inserts
      3. Summary
    9. Chapter 7: Running Queries
      1. BigQuery Query API
      2. BigQuery Query Language
      3. Summary
    10. Chapter 8: Putting It Together
      1. A Quick Tour
      2. Mobile Client
      3. Log Collection Service
      4. Dashboard
      5. Summary
    11. Part III: Advanced BigQuery
      1. In This Part
    12. Chapter 9: Understanding Query Execution
      1. Background
      2. Storage Architecture
      3. Query Processing
      4. Architecture Comparisons
      5. Summary
    13. Chapter 10: Advanced Queries
      1. Advanced SQL
      2. BigQuery SQL Extensions
      3. Query Errors
      4. Recipes
      5. Summary
    14. Chapter 11: Managing Data Stored in BigQuery
      1. Query Caching
      2. Result Caching
      3. Table Snapshots
      4. AppEngine Datastore Integration
      5. Metatables and Table Sharding
      6. Summary
    15. Part IV: BigQuery Applications
      1. In This Part
    16. Chapter 12: External Data Processing
      1. Getting Data Out of BigQuery
      2. AppEngine MapReduce
      3. Querying BigQuery from a Spreadsheet
      4. Summary
    17. Chapter 13: Using BigQuery from Third-Party Tools
      1. BigQuery Adapters
      2. Scientific Data Processing Tools in BigQuery
      3. Visualizing Data in BigQuery
      4. Summary
    18. Chapter 14: Querying Google Data Sources
      1. Google Analytics
      2. Google AdSense
      3. Google Cloud Storage
      4. Summary
    19. Introduction
      1. Overview of the Book and Technology
      2. How This Book Is Organized
      3. How to Read This Book
      4. Tools You Need
      5. Supplemental Materials and Information
    20. End User License Agreement