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

Google BigQuery Data Types

BigQuery is an analytics data warehouse that stores structured data. Structured data is data that is organized and can be outlined via a schema. Similar to an RDBMS, data in BigQuery is organized into rows and columns for the user to query, but the underlying storage is different. BigQuery uses columnar storage to store values of rows and columns, similar to other data warehouse systems on the market. BigQuery can ingest most of the common data types supported by most relational database management systems. Unlike traditional RDBMSes, BigQuery cannot enforce constraints between tables since it is mainly designed for reporting not transactions. BigQuery is not suitable for transactions because it does not support features such as constraints, integrity, and indexes (such as traditional RDBMS); also, latency is high for create and update operations.