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Introduction
If you want to get the most out of any tool, whether it is a database or a rotary power drill, it is important to know how it works. This lets you develop an intuition about how you can use the tool effectively. The goal of this book is to help you develop an intuition about BigQuery, which can enable you to make your own decisions about when BigQuery is the right tool for the job, how you can improve your query effi ciency, and how you can apply BigQuery in unanticipated situations.
It is also important to have good examples that you can incorporate into your code. This book provides source code to help you start using BigQuery in your applications and query examples that can help you solve complex problems in SQL. In addition, we show you how to write code to get your data in, how to query and visualize that data, and how to get it out again.
How This Book Is Organized
Chapters 1–4 (fundamentals): For anyone not familiar with BigQuery, these chapters describe what BigQuery is and how to start using it. Chapter 4, “Understanding the BigQuery Object Model,” is important because the fundamental abstractions in BigQuery differ slightly from other relational database systems.
Chapter 5, “Talking to the BigQuery API”: This chapter gives an overview of the HTTP API that you’ll use if you write code to talk to BigQuery.
Chapter 6, “Loading Data”: If you want to get your data into BigQuery, read this chapter.
Chapter 7, “Running Queries”: You may want to skim the API portion, but you probably shouldn’t skip it completely because it describes what is possible via the API. (And all the API features should be available in the web UI or in the bq command-line client.) Some table management operations that you may expect to be in SQL (such as creating a temporary table) are done via the API in BigQuery. This chapter also discusses how BigQuery SQL is different from standard SQL and walks through a number of BigQuery queries.
Chapter 8, “Putting It Together”: This chapter walks you through an end-to-end AppEngine application that uses BigQuery for logging, dashboarding, and ad-hoc querying. If you write code that uses BigQuery, the online resources for this chapter will be particularly interesting because you may cut and paste a lot of the code that is provided.
Chapter 9, “Understanding Query Execution”: This chapter describes the architecture of the systems that underlie BigQuery. If you want to write good queries and understand why query X is faster than query Y, this chapter is important. Most users of relational databases develop an intuition about how to write effi cient queries, and because BigQuery uses a different fundamental architecture, some of these previous intuitions could get you in trouble. This chapter can help you develop similar intuition about the types of queries that can run well in BigQuery.
Chapter 10, “Advanced Queries”: This chapter shows some queries that you might not think of writing and provides advanced query recipes. You may want to refer back to this chapter when you run into data modeling or query problems.
Chapter 11, “Managing Data Stored in BigQuery”: You might want to skip or skim this chapter, but the portions on how to partition your data or how to make use of the query cache may be useful.
Chapter 12, “External Data Processing”: The second half of this chapter, which describes running queries from Microsoft Excel and Google Spreadsheets, will likely be interesting if your organization uses a lot of spreadsheets.
Chapter 13, “Using BigQuery from Third-Party Tools”: You should read this chapter if you’re interested in data visualization, client-side encryption, R, or using BigQuery via ODBC.
Chapter 14, “Querying Google Data Sources”: If you have data from a Google project (AdSense, Google Analytics, or DoubleClick) that you want to query, this is the chapter for you
