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

Sanitizing data

Most data warehousing projects follow a standard process. This process involves the extraction of data from a data source, the transformation of this data to both the standards of the data warehouse and the requirements of the end user, and the loading of data into the resulting database table. This process is more commonly known as the Extract, Transform, Load Process, or ETL for short.

The transformation step is important for a few reasons:

  • Decoding of encoded values (that is, converting values of W and E to West and East)
  • Calculation of values (that is, calculating the average order value by dividing the revenue by the count of orders)
  • Splitting separated lists into individual columns
  • Aggregation
  • Data validation, either in the form of invalidating incorrect values or as reprocessing of incorrect data

If you are using files to load data into BigQuery then...