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Learning Google BigQuery
Learning Google BigQuery

Learning Google BigQuery: A beginner's guide to mining massive datasets through interactive analysis

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Full star icon Full star icon Full star icon Half star icon Empty star icon 3.3 (4 Ratings)
Paperback Dec 2017 264 pages 1st Edition
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Arrow left icon
Profile Icon Haridass Profile Icon Berlyant Profile Icon Brown
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$38.99
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.3 (4 Ratings)
Paperback Dec 2017 264 pages 1st Edition
eBook
$20.98 $29.99
Paperback
$38.99
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Free Trial
Renews at $19.99p/m
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Learning Google BigQuery

Google Cloud SDK

Google Cloud Platform provides an SDK developed in Python to manage the resources in the Cloud. The framework is available for Windows, Linux, and macOS. Python 2.7 is a requisite for installing this SDK. The SDK provides command-line utilities to manage and interact with various services on Google Cloud.

The following are the three command-line utilities available in SDK:

  • gsutil: This is the command-line utility to interact with Google Cloud Storage
  • bq: This is the command-line utility to interact with Google BigQuery
  • gcloud: This is the command-line utility to interact with all other services on Google Cloud

Installing Google Cloud SDK

The installers are available for Windows, Linux, and macOS. Since Linux has various distributions, some manual command execution is needed for installing and configuring the Google Cloud SDK on Linux.

Installing Google Cloud SDK on Windows

Google Cloud SDK for Windows comes with a friendly installer and it also comes with an option to install Python which is a prerequisite to run the commands in Google Cloud SDK:

  1. Download the installer from the link provided: https://cloud.google.com/sdk/docs/quickstart-windows. The installer is a GUI-based utility which will install the requisites for the SDK, and the SDK with default configuration.
  1. In the installation wizard, choose the Bundled Python...

gsutil for Google Cloud Storage

gsutil provides options to manage files, folders, and buckets in Google Cloud Storage. The first step in moving your data to Google Cloud and Google BigQuery is to export the data and upload to Google Cloud Storage:

  • Manually via the browser if it is small
  • Automate it for basic scenarios using gsutil, which comes with Google Cloud SDK
  • The third option will be to use the Google Cloud Storage API to perform advanced automation

Before using the gsutil command, make sure that the project and credentials configured in the Google Cloud SDK are pointing to the project and account which you intend to use by typing the following command:

gcloud info

We will now look at the features available with gsutil:

  • To see the list of options provided by gsutil, type the following command:
gsutil help
  • The available commands section shows the command...

Using the bq utility for BigQuery

The bq command-line utility is used to interact with the Google BigQuery service on Google Cloud Platform:

  • Use the following command to check the version of the bq utility once the SDK is installed:
bq version
  • Type the following command to confirm which project the bq utility will use. The bq utility shares settings with the gcloud utility. If you wish to change the project, then run the command in the second line and choose the project you want to work on:
gcloud info
gcloud init
If an older version of Google Cloud SDK is installed on the machine, then run the bq init command to choose the default project for the bq utility to use. Use the bq help option to see the complete details of the command, its options, and its switches.
  • The first step in using BigQuery is to create a dataset in a project and then create tables...

Using the gcloud utility

The gcloud utility is used to interact with the rest of the services on the Google Cloud Platform, other than BigQuery and Google Cloud Storage. The commands in the gcloud utility are grouped for each service. The following are the service groups for some of the services on the Google Cloud Platform:

Service group

Google Cloud service

App

App Engine standard and flexible environment

Compute

Compute Engine to manage virtual machines

Container

Container Engine to manage containers and clusters

Dataflow

Manage Cloud Dataflow services for ETL and data processing

Dataproc

Manage Cloud Dataproc service which consists of Apache Hadoop, Spark, Pig, and Hive

Datastore

Manage Cloud Datastore service which creates entities on a NoSQL database

SQL

Manage Cloud SQL service which consists of MySQL or PostgreSQL databases...

Connecting to Cloud SQL using gcloud

There is more than one way to connect to a Cloud SQL instance from your machine, but the two most frequently used options are in the following list. The listed prerequisites for both the options require the MySQL client libraries to be installed on the local machine:

  • Adding the client machine IP address to authorized networks in Google Cloud Console
  • Installing a proxy script on the client machine and using that to connect to Google Cloud SQL

Authorizing the client machine via Google Cloud Console

Get the IP address of the client machine by opening the browser and navigating to this URL: http://ipv4.whatismyv6.com/. Note down the IP address shown on this page for your machine. Open Google...

Deploying to Google App Engine

To deploy to Google App Engine, use the app command group. The App Engine allows us to host websites developed in PHP, Java, Go, or Python in the standard environment. To deploy a sample app to the App Engine instance that was created in Chapter 1, Google Cloud and Google BigQuery, follow the steps listed here:

  1. Download the sample PHP file and app.yaml file from the GitHub URL (https://github.com/hthirukkumaran/Learning-Google-BigQuery/tree/master/chapter2/phpapp) to a folder on the local computer. This PHP code connects to the Cloud SQL instance of the same project, executes a sample query, and displays the result.
  2. Open the index.php file and modify the $dsn variable value to point to your Cloud SQL instance. To get the full qualified Cloud SQL instance name, open the Cloud SQL instance in your project. Copy the value of the instance connection...

Summary

This chapter covered the Google Cloud SDK and the utilities that come with it. The samples in this chapter provided an overview of how to interact with Google Cloud Services and how to manage your resources on Google Cloud Platform using the Google Cloud SDK. Use the utilities to write your batch programs that you run from your on-premises infrastructure.

The next chapter covers data types in Google BigQuery, how to use them when creating custom tables, and how to import and export the data using the bq utility that was covered in this chapter.

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Key benefits

  • Get started with BigQuery API and write custom applications using it
  • Learn how BigQuery API can be used for storing, managing, and query massive datasets with ease
  • A practical guide with examples and use-cases to teach you everything you need to know about Google BigQuery

Description

Google BigQuery is a popular cloud data warehouse for large-scale data analytics. This book will serve as a comprehensive guide to mastering BigQuery, and how you can utilize it to quickly and efficiently get useful insights from your Big Data. You will begin with getting a quick overview of the Google Cloud Platform and the various services it supports. Then, you will be introduced to the Google BigQuery API and how it fits within in the framework of GCP. The book covers useful techniques to migrate your existing data from your enterprise to Google BigQuery, as well as readying and optimizing it for analysis. You will perform basic as well as advanced data querying using BigQuery, and connect the results to various third party tools for reporting and visualization purposes such as R and Tableau. If you're looking to implement real-time reporting of your streaming data running in your enterprise, this book will also help you. This book also provides tips, best practices and mistakes to avoid while working with Google BigQuery and services that interact with it. By the time you're done with it, you will have set a solid foundation in working with BigQuery to solve even the trickiest of data problems.

Who is this book 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.

What you will learn

  • Get a hands-on introduction to Google Cloud Platform and its services
  • Understand the different data types supported by Google BigQuery
  • Migrate your enterprise data to BigQuery and query it using the legacy and standard SQL techniques
  • Use partition tables in your project and query external data sources and wild card tables
  • Create tables and data sets dynamically using the BigQuery API
  • Perform real-time inserting of records for analytics using Python and C#
  • Visualize your BigQuery data by connecting it to third party tools such as Tableau and R
  • Master the Google Cloud Pub/Sub for implementing real-time reporting and analytics of your Big Data
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Length: 264 pages
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Table of Contents

8 Chapters
Google Cloud and Google BigQuery Chevron down icon Chevron up icon
Google Cloud SDK Chevron down icon Chevron up icon
Google BigQuery Data Types Chevron down icon Chevron up icon
BigQuery SQL Basic Chevron down icon Chevron up icon
BigQuery SQL Advanced Chevron down icon Chevron up icon
Google BigQuery API Chevron down icon Chevron up icon
Visualizing BigQuery Data Chevron down icon Chevron up icon
Google Cloud Pub/Sub Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.3
(4 Ratings)
5 star 25%
4 star 25%
3 star 0%
2 star 50%
1 star 0%
Robert Robinson Jan 26, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is easy to understand to know what is Big query and how to use Google BigQuery, Google services with good examples.This book covers in beginning how to start with Google cloud services and their types of services. It helped me to do project in Google App Engine and Google BigQuery. We can learn BigQuery API with examples and the current capabilities of the platform.The great thing in this book is we can choose the language in C#.Net or Python to get into Google BigQuery API.Finally it's a great book for BigQuery beginners.
Amazon Verified review Amazon
FSTB Jan 29, 2018
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
This is a step by a step book that give you the bases and the power through all the reading. As you read and follow the exercises, you quickly learn and understand the meaning behind BigQuery. Easy to read, easy to learn. The author gives tips and lessons based on their experience. Using this data warehouse solution, you will save time and get into work very quickly. You will see results as you follow the guides from this book.
Amazon Verified review Amazon
Eike Pierstorff Jun 07, 2018
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
This is not a "guide to mining massive datasets", this is a guide to run very basic SQL queries against a high performance cloud database; basic SQL does not suddenly become "data mining" because you run it on a cloud database, or via an API. If you really need to have explained to you what "SELECT" does in a SQL statement then BigQuery might not be a good tool for you in the first place (and don't event get me started on the so called "Advanced SQL" section).What's left is an okay-ish step by step manual on how to create a BigQuery project and set up API access, so if you prefer spending some 20 bucks over searching for ten minutes to find the appropriate support pages this book might be for you.This book might make sense if you take the "beginner" in the title to mean "first time database users" (which would be akin to "heart surgery for first time knife users"). I mistook "beginner" to mean "BigQuery beginner" (i.e. I thought I could expand existing SQL skills into data mining), but even so, for SQL starters there is just no point to start with BigQuery. Basically any SQL manual will be more useful.
Amazon Verified review Amazon
George A. Quintas Jan 25, 2018
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
The kindle version is not working on Kindle Cloud Reader. I only able to read from location 1 to location 719 and beyond that the contents are all unreadable and I lost interest reading this book and did not bother to read the rest of the pages (location 1429 on wards are readable). I need a refund please or send me a complete working copy. So far the readable locations gave me the information that I need but still I need a complete book.
Amazon Verified review Amazon
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