Preface

Learning Google BigQuery is filled with unique and comprehensive information about Google's petabyte-scale data warehouse solution, Google BigQuery, hosted on Google Cloud Platform. This book also covers other services on Google Cloud Platform and how to integrate them with Google BigQuery.

You'll learn how to get started with Google Cloud Platform and try out various services in Google Cloud Platform. The book explains how to migrate your existing data from your enterprise to Google BigQuery, optimize your data in BigQuery, query the data here, and connect BigQuery data to various sources for reporting and visualization. You will also learn how to implement real-time streaming of data from an application running in your enterprise to BigQuery, which will help you accomplish your vision of real-time reporting.

In addition, all the code samples in this book are available in Packt's GitHub account. This book also provides tips, best practices, and mistakes to avoid when working with Google BigQuery and services that interact with it. We hope this book helps you to move your data to Google BigQuery and develop your envisioned reporting and analytics solutions to unleash the power of data. Any updates to this book will be automatically made available to you by the Packt platform.

What this book covers

Chapter 1, Google Cloud and Google BigQuery, is a hands-on demo of App Engine, Cloud SQL, BigQuery, Cloud datastore, compute engine, and Google Cloud Storage.

Chapter 2, Google Cloud SDK, covers how to install and configure the Google Cloud SDK and use various utilities provided in the SDK to interact with App Engine, Cloud SQL, BigQuery, and Google Cloud Storage.

Chapter 3, BigQuery Data Types, illustrates various data types supported in Google BigQuery and how to migrate your data to BigQuery.

Chapter 4, BigQuery SQL Basic, covers how to query the data using both legacy SQL and standard SQL, and how to merge data from various tables using queries.

Chapter 5, BigQuery SQL Advanced, shows how to use partition tables in your project and query an external data source on Google Cloud (such as Google Cloud Storage) from within BigQuery. We cover querying of wild card tables, user-defined functions, views, and using nested and repeated types in our tables to support importing JSON data.

Chapter 6, Google BigQuery API, teaches you how to use BigQuery API to create tables and datasets dynamically. You learn to load data into BigQuery and perform streaming insert of records for real-time analytics using Python and C#. Permissions, users, and roles are covered in this chapter.

Chapter 7, Visualizing BigQuery Data, shows you how to visualize your data by connecting it to various frontend tools, such as Tableau and Google Data Studio. We write custom programs in R. 

Chapter 8, Google Cloud Pub/Sub, covers the use of the Cloud Pub/Sub messaging system to log messages from various applications and its use to implement real-time reporting and analytics. This chapter also covers Cloud Dataprep, which helps prepare the data for loading into BigQuery.

What you need for this book

For this book all you would require is the Google Cloud SDK, the browser of your choice (Chrome is recommended), and an editor that supports PHP coding, and you're all set to begin. It is also recommended to learn SQL basics for writing advanced queries in Google BigQuery. This book uses Google BigQuery Public Dataset for demos.

Who this book is for

If you are a developer, data analyst, or a data scientist looking to run complex queries over thousands of records in seconds, this book will help you. No prior experience of working with BigQuery is assumed.

Conventions

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "If the table already exists then the bq utility will throw the error Already Exists: Table project-id:datasetname.tablename."

A block of code is set as follows:

SELECT year(pickup_datetime) as trip_year, count(1) as trip_count
FROM [nyc-tlc:yellow.trips] 

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

{  "rule":
  [
    {
      "action": {"type": "Delete"},
      "condition": {"age": 30}
    }
  ]
}

Any command-line input or output is written as follows:

sudo apt-get install google-cloud-sdk
apt-cache showpkg google-cloud-sdk 

New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "Choose My Billing Account in the Project or Billing account drop-down and check the Include credit as a budget expense option".

Warnings or important notes appear like this.

Tips and tricks appear like this.

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Downloading the example code

You can download the example code files for this book from your account at http://www.packtpub.com. If you purchased this book elsewhere, you can visit http://www.packtpub.com/support and register to have the files emailed directly to you. You can download the code files by following these steps:

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The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Learning-Google-BigQuery. We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Errata

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Questions

If you have a problem with any aspect of this book, you can contact us at [e-mail], and we will do our best to address the problem.