A Basic Explanation of BigQuery

Harrison Mateika - September 5, 2023

As Google is continuously shifting to the new GA4 paradigm they have been pushing integration to a product that many people are not familiar with. This product is BigQuery. BigQuery has been showing itself to become more and more central to Google’s web analytics strategy in the following ways:

  1. Google is allowing a free connection between GA4 and BigQuery for all of their users. This service was only previously available to GA 360 customers when Universal Analytics was their product.
  2. Google has been instituting more limits in the amount of data that can be pulled into dashboards from GA4.
  3. Google’s flagship dashboard product has rebranded from Data Studio to Looker Studio, suggesting a shift toward their Business Intelligence product Looker which taps primarily into BigQuery as a data source.

This article will help to provide some high-level explanation of what BigQuery is and what it does, how BigQuery relates to GA4 and what organizations are right for BigQuery.

What is BigQuery?

BigQuery is Google Cloud Product’s (GCP’s) data warehouse product that is meant to store and access large amounts of data at a low cost. The data is often illustrated in a familiar tabular format of rows and columns; however, data can also be stored in other less structural forms.

This data can be accessed through a database programming language called Structured Query Language (SQL). Unlike other computer programming languages, this language is specifically designed to query databases and for ease of readability, making it a language that is not too challenging to start with.

Through SQL, the data can be accessed, manipulated, updated, deleted, and can be used to sort, rank, make calculations out of data, and provide statistical analysis. Accessing data through SQL is often referred to as querying the data.

These SQL queries can be utilized in a variety of different avenues. A SQL query can be implemented directly on BigQuery’s user interface, through GCP’s Cloud Shell, through BigQuery’s API, and directly on BI platforms and dashboards like Looker, Looker Studio, and Tableau.

BigQuery is also capable of querying from data sources that are located outside of its storage. These include data stored in Cloud Storage, relational databases like Cloud SQL, and Google Sheets. It also includes features like the ability to transform data for feature engineering, create machine learning models, and allows for access to other automated machine learning and AI platforms like AutoML.

BigQuery and GA4

Google offers a free connection from GA4 to BigQuery. This connection remains free when the data is uploaded every 24 hours (there is a quota of one million events though). There is also another option to make that connection a real-time upload (with no quota), however there is added cost associated with it. This free connection allows for users to tap into the GA4 data through BigQuery.

While setting up GA4 with BigQuery is free, additional costs can occur overtime. These costs can be split into two areas: querying and storage. Both of these costs are normally low in most instances, however there are ways that the costs can get out of control. Therefore, it’s best to always make sure that there are cost-control measures such as query quotas in place.

Despite the cost though, there are a number of reasons why BigQuery can be an excellent partner with GA4 including the ability to completely integrate your data, create custom reporting, and take advantage of the machine learning components.

What Organization is Right for BigQuery

BigQuery as a solution is right for organizations that want the following:

  1. The desire to view their GA4 data at a deeper level than what GA4 can provide.
  2. The need to integrate GA4 data to platforms from various solutions such as their relational databases, CRMs, or other non-web analytics reports.
  3. The desire to use predictive analytics on their GA4 data.
  4. The need to store data for GA4 Explore-like analysis beyond the 14-month data retention period.

Furthermore, it is also important that organizations understand whether there are any budget constraints regarding their BigQuery storage and querying costs. While BigQuery offers free tiers for both, it is important to assess ahead of time the cost that these factors can have and when these costs may hit. Should an organization not have the budget to implement BigQuery, it is best to see if other solutions are available.

BigQuery is a complex tool that can easily get unwieldly and out-of-hand if not managed well. If you need help in understanding BigQuery and how it can work best for your business, please contact us at info@morevisibility.com.

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