Learning Google BigQuery : a beginner's guide to mining massive datasets through interactive analysis / Thirukkumaran Haridass, Eric Brown.

Format:

Book

Author/Creator:

Haridass, Thirukkumaran, author.

Brown, Eric, author.

Language:

English

Subjects (All):

Google Analytics.

Data mining.

Physical Description:

1 online resource (240 pages) : illustrations (some color)

Edition:

1st edition

Place of Publication:

Birmingham, England : Packt, 2017.

System Details:

Mode of access: World Wide Web.

text file

Biography/History:

Haridass Thirukkumaran: Thirukkumaran Haridass currently works as a lead software engineer at Builder Homesite Inc. in Austin, Texas, USA. He has over 15 years of experience in the IT industry. He has been working on the Google Cloud Platform for more than 3 years. Haridass is responsible for the big data initiatives in his organization that help the company and its customers realize the value of their data. He has played various roles in the IT industry and worked for Fortune 500 companies in various verticals, such as retail, e-commerce, banking, automotive, and presently, real estate online marketing. Brown Eric: Eric Brown currently works as an analytics manager for PMG advertising in Austin, Texas. Eric has over 11 years of experience in the data analytics field. He has been working on the Google Cloud Platform for over 3 years. He oversees client web analytics implementations and implements big data integrations in both Google BigQuery and Amazon Redshift. Eric has a passion for analytics, and especially for visualization and data manipulation through open source tools such as R. He has worked in various roles in various verticals, such as web analytics service providers, media companies, real-estate online marketing, and advertising.

Summary:

Get a fundamental understanding of how Google BigQuery works by analyzing and querying large datasets About This Book 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 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. 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 In Detail 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 ...

Contents:

Cover

Title Page

Copyright

Credits

Foreword

About the Authors

About the Reviewers

www.PacktPub.com

Customer Feedback

Dedication

Table of Contents

Preface

Chapter 1: Google Cloud and Google BigQuery

Getting started with Google Cloud

Overviewing Google Cloud Platform services

Google Cloud storage and its features

Learning Google BigQuery

Working with the browser

Running your first query

BigQuery public datasets

Getting started with Cloud SQL

Cloud Datastore

Google App engine

App engine standard environment

App engine flexible environment

Google container engine

Google compute engine

Summary

Chapter 2: Google Cloud SDK

Installing Google Cloud SDK

Installing Google Cloud SDK on Windows

Installing Google Cloud SDK on macOS

Installing Google Cloud SDK on Linux

gsutil for Google Cloud Storage

Using the bq utility for BigQuery

Using the gcloud utility

Connecting to Cloud SQL using gcloud

Authorizing the client machine via Google Cloud Console

Connecting using a proxy script

Exporting Cloud SQL databases and tables

Deploying to Google App Engine

Chapter 3: Google BigQuery Data Types

Supported data types

Data type considerations

Converting data

Sanitizing data

When to transform your data? Before or after loading to BigQuery?

Arithmetic Operators

Comparison Operators

Date Time Functions

String Functions

Regular Expression Functions

Functions for transformation

Mastering transformation with User-Defined Functions

Some considerations when using UDFs

UDF format

Further Reading

Chapter 4: BigQuery SQL Basic

The BigQuery interface

Error checking

Querying in BigQuery

Types of queries

Querying public data

Basic SQL syntax

Commenting in BigQuery SQL.

SELECT

FROM

WHERE

GROUP BY

ORDER BY

HAVING

Qualifying tables in query

DISTINCT

BigQuery SQL functions

WITHIN

OMIT RECORD IF

ROLLUP

Joining tables in BigQuery

Inner join

Left Outer join

Right Outer join

Full Outer join

Cross join

UNION, UNION ALL, and UNION DISTINCT

Adding your own data in BigQuery

Creating a table

Inserting data to a table

Updating data in a table

Resetting a value

Deleting data from a table

Further reading

Chapter 5: BigQuery SQL Advanced

Partition tables

Creating a partition table using a GUI

Creating a partition table using Google Cloud SDK

Querying data in a partition table

Using partition tables in your projects

Querying external data sources using BigQuery

Creating the table definition

Querying data from external data sources

Wildcard tables

User-defined functions

Views

Querying nested and repeated records

Chapter 6: Google BigQuery API

Accessing Google BigQuery

Introducing Google APIs explorer

Getting credentials for API access

Creating a service account

Programming with BigQuery API in C# .NET

Authenticating the service account

Listing all datasets and all tables in the project

Creating a new dataset in the project

Creating a new table within a dataset

Loading data from a file in Google Cloud Storage to a BigQuery table

Executing a query and displaying the result

Executing the query and saving the result in a new table

Streaming insert of rows

Programming with BigQuery API in Python

Importing data from a file in Google Cloud Storage to a BigQuery table.

Executing a query and displaying the result

Execute query and copy results to a new table

Roles and permissions

Chapter 7: Visualizing BigQuery Data

Why is data visualization important?

The danger of summary statistics

Making data visualization work for you

Three tools for visualizing BigQuery data

Simple yet basic - Google Data Studio

Getting started

Making a scatterplot in Data Studio

Making a map in Data Studio

Other features of Data Studio

Simple, fairly flexible, but with a cost - Tableau

Map charts in Tableau

Create a word cloud in Tableau

Complex but with considerable flexibility - the R programming language

Chapter 8: Google Cloud Pub/Sub

Introduction

Getting started with Cloud Pub/Sub

Cloud Pub/Sub via Google Cloud Console

Cloud Pub/Sub via Google Cloud SDK

Cloud Pub/Sub pricing

Message output formats

Importing message data into BigQuery

Google Cloud Dataprep

Index.

Notes:

Includes index.

Includes bibliographical references and index.

Description based on online resource; title from PDF title page (ebrary, viewed February 5, 2018).

OCLC:

1021185653