Optimizing Google BigQuery

BigQuery is Google's incredibly fast, secure, and surprisingly inexpensive data warehouse, but there are ways to make it even faster, cheaper, and more secure. 

Here are some examples of what you will learn in this course:

• BigQuery can process billions of rows in seconds, but only if you break the rules of relational database design.
• If you are analyzing relatively small amounts of data, then your queries won’t cost very much, but if you regularly analyze huge datasets, then your costs can add up quickly. However, with a few adjustments to how you store your data in BigQuery, you can run queries for a fraction of the cost.
• To give you the flexibility to implement fine-grained security, BigQuery has several layers of access control capabilities, but they can be confusing, so I’ll show you which ones to use to meet your organization’s requirements.

This is a hands-on course where you can follow along with the demos using your own Google Cloud account or a trial account.

Learning Objectives

• Reduce your BigQuery costs by reducing the amount of data processed by your queries
• Create, load, and query partitioned tables for daily time-series data
• Speed up your queries by using denormalized data structures, with or without nested repeated fields
• Implement fine-grained access control using roles and authorized views

Intended Audience

• Database administrators
• Anyone who wants to learn how to get the most out of Google BigQuery

Prerequisites

To get the most out of this course, you should already have some experience with BigQuery. If you don’t, then please take Introduction to Google BigQuery first.

Resources

The GitHub repository for this course can be found at https://github.com/cloudacademy/optimizing-bigquery/.