Google BigQuery Training

Looking for the connection between Google Analytics 360 & BigQuery? We'll help you find it!

This course is designed to give a complete introduction and overview of using Google Analytics 360 data in Google BigQuery. If you’re a marketer, data scientist, or engineer and need direct access to the detailed data that underlies your GA360 reports, this course is for you.

Our trainers are actually consultants here at Bounteous, working every day in the materials they’ll be teaching – so you’ll get real-world tips and someone who can answer the difficult questions.

Google BigQuery 101

NOTE: While this course will provide a general overview of BigQuery and Google Cloud Platform, the specific focus is on Google Analytics data. The examples will focus on web analytics.

This course is designed to give a complete introduction and overview to using Google Analytics 360 (GA360) data in BigQuery. GA360 provides a native integration to export the data it collects to BigQuery, a cloud database that’s part of Google Cloud Platform.

Prior to taking this course, you should be familiar with Google Analytics standard reports and basic dimensions & metrics. It’s helpful (but not required) to have some basic familiarity with SQL and Data Studio. You do not need GA360 and BigQuery configured for this course, we’ll be using the Google BigQuery Demo Account.

We begin with the big-picture basics:

  • Google Cloud Platform, Google Marketing Platform, and their products and integrations.
  • GCP management and costs.
  • Google Analytics 360 integration with BigQuery.
  • Applications of digital analytics data in BigQuery including reporting, analysis, dataset integration, and data science modeling.

BigQuery Introduction
We start our hands-on interaction with BigQuery by looking at the web interface and query language, including:

  • Google Cloud Console and BigQuery UI.
  • BigQuery data structure.
  • BigQuery SQL dialects.

Google Analytics Schema in BigQuery
Next, we explore the details of the Google Analytics data in BigQuery and how it’s structured:

  • GA schema overview.
  • Dealing with nested and repeated fields.
  • Understanding hit types and how records correspond to GA data.

Deriving Metrics Using Aggregations
A large portion of the day is spent performing hands-on queries in BigQuery to understand Google Analytics data and how to calculate metrics from it, such as:

  • Replicating basic GA session metrics including users, sessions, pageviews, bounce rate, etc.
  • Using BQ’s SQL features to query date ranges.
  • Unnesting hit-level and product-level data and calculating metrics such as landing pages, time on page, and more.
  • Using custom dimensions and custom metrics.
  • Limitations of GA’s standard metrics and examples of alternative calculations.
  • Best practices for structuring queries for readability.

Importing & Exporting Data
Finally, we’ll look at applications for using BigQuery to bring together data for analysis, visualization, and modeling:

  • Importing and exporting data.
  • Scheduling and automation.
  • Integrations with other Google data sources (Firebase, Google Ads, Campaign Manager, YouTube, and others).
  • Connecting data visualization tools including Data Studio.