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

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

There are a couple of phases in the ETL process when an analyst might want to transform their data. BigQuery is a very sophisticated data warehouse system. Because of this, BigQuery has already implemented a number of functions useful for transformation. With that said, BigQuery does not have every function that you might find in other data warehouses or programming languages. Also, BigQuery's automatic type detection sometimes might coerce a data type with unwanted results. Because of this, the analyst will need to make a decision as to how much transformation he or she wants to apply to the data prior to loading it in BigQuery.

Chapter 8 cover Google Cloud Dataprep which is a server less service which can load the data from a file, transform it and insert into BigQuery. The ETL jobs developed using Cloud...