Google Analytics 4 (GA4) is an essential tool for tracking and understanding user interactions on websites and apps. BigQuery (BQ), a powerful data warehouse, enables comprehensive data analysis through fast SQL queries. For those who have recently integrated GA4 with BigQuery, importing historical data can be a crucial step to get complete insights. In this blog, we will explore various options for importing historical data from GA4 to BigQuery and identify the best approach.
If you've recently linked GA4 with BQ, you may find that your BQ project is missing some historical data. This is because, by default, GA4 data is only imported to BQ starting from the date of your initial connection. Historical data plays a crucial role in providing context and trends for insightful analysis. Importing this data into BigQuery allows you to delve into long-term patterns and comparisons, offering a comprehensive view of user behavior over time.
A Google Cloud Platform account with billing enabled.
A BQ project with billing enabled where you will store the GA4 data.
A connection between your GA4 property and your BQ project.
A dataset in your existing project for storing historical GA4 data.
There are four methods available for preserving historical data from GA4 to BQ. Let's examine each of them in depth.
One of the simplest approaches to backfilling GA4 data involves manually exporting it and importing it into BQ. This method includes extracting data from GA4 via the reporting interface, saving it in a compatible format like CSV or Google Sheets, and then transferring it into BigQuery or your data warehouse.
Utilize a Google Sheets add-on, like Google's official tool or third-party extensions, to craft, customize, and export reports to CSV format. Google provides a complimentary add-on, while tools like GA4 Magic Reports and GA4 Reports Builder for Google Analytics offer a range of reporting features. After installing the add-on in Google Sheets, you can effortlessly create, customize, and export reports to CSV or share them. Explore the functionalities and distinctions of various Google Analytics 4 (GA4) add-ons for an enhanced reporting experience:
Feature / Extensions |
GA4 Magic Reports |
GA4 Reports Builder for Google Analytics (Google’s official add-on) |
GA4 - Reporting for Google Analytics 4 (Adformatic) |
Integration |
Integrated with Google Analytics 4 |
Integrated with Google Analytics 4 |
Integrated with Google Analytics 4 |
Cost |
Free |
Free |
Subscription-based pricing model |
Automation |
Limited Automation Capabilities |
You cannot configure reports to import Google Analytics data automatically. |
Automated Report Scheduling and Email notifications |
Customization |
Customization options available(Limited) |
Not allow |
Customizable Report templates and configurations |
User Interface |
User Friendly |
User Friendly |
User Friendly |
Multi-Account Support |
N/A |
N/A |
Support reporting across multiple accounts |
Dimensions/Metrics filtering or sorting |
Allow |
Not allow, manually create, add, and need to run the report again |
Allow |
Performance Max Reports |
N/A |
Ability to create performance max reports |
N/A |
Dependency on the Third party |
No |
No |
Yes |
Exporting Google Analytics 4 (GA4) data to BigQuery (BQ) using the Google Analytics Data API involves querying GA4 data through the API and inserting it into BQ tables. This process requires setting up a Google Cloud project, enabling APIs, creating a service account, linking GA4 property to BQ, writing code to authenticate and execute queries, and optionally scheduling updates. By leveraging the Google Analytics Data API, businesses can seamlessly transfer GA4 data to BQ for advanced analysis and reporting.
Here's a brief overview of the process:
Third-party ETL tools provide a robust option for backfilling GA4 data, offering a comprehensive solution for data extraction, transformation, and loading. These tools streamline the integration of GA4 data into your analytics platform or data warehouse, ensuring scalability, reliability, and advanced data management features. They come with built-in capabilities for data transformation and scheduling, making them suitable for large-scale data backfilling needs. Additionally, they support various data sources and destinations, including GA4 and BigQuery. However, these tools do have some drawbacks, such as subscription costs and dependency on external service providers for ongoing support and maintenance.
Using the Google Analytics API is often the best approach for importing historical data from GA4 to BigQuery. Here’s why:
While the initial setup may be complex and require technical expertise, the long-term benefits of using the Google Analytics API for data import far outweigh these challenges. For those with smaller datasets or less technical resources, starting with manual exports or add-ons can be a practical first step, with the option to scale up to API-based solutions as needed. Third-party ETL tools can also be a viable option if budget allows and immediate scalability is a priority.