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

The danger of summary statistics

When performing analyses, most analysts will use what is called summary statistics. Summary statistics is defined as descriptive statistics that are used to summarize a larger set of observations. These types of statistics are usually used because the larger set of observations is too large to analyze efficiently. For instance, an SEO analyst might use the average click-through as a measure of performance for their SEO efforts on two separate days of analysis.

The analyst finds that the average click-through rate for both days was identical at 8.4. However, here is what is revealed when visualizing the data:

The analyst was able to see how the data was extremely different when visualized rather than just summarized.