Book Image

Learning Google BigQuery

By : Thirukkumaran Haridass, Mikhail Berlyant, Eric Brown
Book Image

Learning Google BigQuery

By: Thirukkumaran Haridass, Mikhail Berlyant, Eric Brown

Overview of this book

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.
Table of Contents (9 chapters)
Free Chapter
Google Cloud and Google BigQuery

String Functions

The + operator can be used to concatenate string in legacy SQL but in standard SQL CONCAT function has to be used:


The LENGTH function returns the length of the string passed as argument. The following query returns 5 as output:


The REPLACE function will replace the specified text with the replacement text. The following query will output ***** this value:

SELECT REPLACE('[email protected]','reachme','*****')

The SPLIT function splits the string by delimiter specified and returns an array of values. The output of the query is shown in the following screenshot:

SELECT SPLIT('1,2,3,4,5,6',',')