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

Wildcard tables

Wildcard is a way of performing a union on tables whose names are similar and have compatible schemas. The following queries show how to perform wildcard operations on tables in the public dataset bigquery-public-data:new_york provided by Google.

The following query gets the number of trips per year made by a yellow taxi in New York. The query uses UNION ALL on all tables that start with the name tlc_yellow_trips_. If a new table is added for 2017, this query has to be modified to include that table as well. To automatically include tables having similar names in the query, wildcard table syntax can be used. This query uses standard SQL:

SELECT MAX(EXTRACT(YEAR from pickup_datetime)) as TripYear, count(1) as TripCount FROM `bigquery-public-data.new_york.tlc_yellow_trips_2009`
SELECT MAX(EXTRACT(YEAR from pickup_datetime)) as TripYear...