How to Connect, Export, and Integrate Data from GA4 to BigQuery

Easily move your data from Ga4 to BigQuery to enhance your analytics capabilities. With Hevo’s intuitive pipeline setup, data flows in real-time—check out our 1-minute demo below to see the seamless integration in action!

Do you rely heavily on GA4 data for analyzing the metrics of your website engagement? If yes, then you would face problems while collecting all the GA4 data and performing advanced analytics on it.

If you want to gain business-critical insights from your GA4 data, then you can’t simply manipulate it. You need to have access to all your marketing and website data in a centralized repository

This article throws light on two methods for implementing GA4 BigQuery Integration. However, to increase your time to value you can definitely go through the simple two-step process for replicating data from GA4 to BigQuery.

What is BigQuery?

Google Cloud Platform provides BigQuery, Google’s enterprise data warehouse that makes large-scale data analysis accessible to everyone. It is a platform-as-a-service (PaaS) that supports querying using ANSI SQL. It’s a fully managed and serverless data warehouse that empowers you to focus on analytics instead of managing infrastructure.

Advantages of Connecting Google Analytics 4 to BigQuery

1. Raw, Unsampled Data for Better Analysis

Exporting data to BigQuery allows you to access raw, unsampled data and perform more precise and detailed analysis than the aggregated data available directly in GA4.

2. Extended Retention Period

With bigQuery, you can store your GA4 data for as long as you need, beyond the default retention periods in GA4. This extended retention allows you to conduct historical analysis and identify long-term trends, providing security and ease with your data storage.

3. Joins with Other Data Sources

In BigQuery, you can join GA4 data with data from other sources, such as your CRM, sales databases, or third-party APIs. This capability facilitates comprehensive, cross-platform analysis, giving you a more insightful and knowledgeable view of your business performance.

4. Advanced Visualization

BigQuery integrates seamlessly with visualization tools like Google Data Studio, Tableau, and Looker. These tools allow you to create sophisticated dashboards and reports, enabling more profound insights and informed decision-making.

5. GA4 BigQuery Export is free

Google offers free exports of GA4 data to BigQuery, making it an economical choice for businesses to leverage advanced analytics capabilities without incurring additional costs.

Methods to connect Google Analytics 4 to BigQuery

Method 1: Using Hevo Data to Set up GA4 BigQuery Integration

Hevo takes care of all your data preprocessing to set up GA4 BigQuery Integration and lets you focus on key business activities and draw a much more powerful insight on how to generate more leads, retain customers, and take your business to new heights of profitability.

It provides a consistent & reliable solution to manage data in real-time and always has analysis-ready data in your desired destination. 

Hevo Data focuses on two simple steps to connect GA4 BigQuery Integration:

• Step 1: Configure Google Analytics 4 as a Source
• Step 2: Integrate Data into Google BigQuery

Step 1: Configure GA4 as a Source

• Click PIPELINES in the Navigation Bar.
• Click + CREATE in the Pipelines List View.
• In the Select Source Type page, select Google Analytics 4 as the Source.
• In the Configure your Google Analytics 4 Account page, do one of the following:
  • Select a previously configured account and click CONTINUE.
  • Click + ADD GOOGLE ANALYTICS 4 ACCOUNT and perform the following steps to configure an account:
    1. Select your linked Google account.
    2. Click Allow to grant Hevo access to your analytics data.

• In the Configure your Google Analytics 4 Source page, specify the following:

Step 2: Integrate Data into Google BigQuery

• Click DESTINATIONS in the Navigation Bar.
• Click + CREATE in the Destinations List View.
• In Add Destination page select Google BigQuery as the Destination type.
• In the Configure your Google BigQuery Warehouse page, specify the following details:

As can be seen, you are simply required to enter the corresponding credentials to implement this fully automated data pipeline without using any code.

Method 2: Using Google Cloud Platform to Implement GA4 BigQuery Integration

The steps to set up GA4 Bigquery Integration are as follows:

• Step 1: Create a Project in Google BigQuery
• Step 2: Enable GA4 BigQuery Linking
• Step 3: Enable Google Cloud API
• Step 4: Add a Service account
• Step 5: Use Google BigQuery with GA4 data

Step 1: Create a Project in Google BigQuery

• Log in to your Google BigQuery account.
• On the menu bar, click on the arrow beside the name of the project getting displayed.

• A pop-up window will appear with a list of existing BigQuery projects. In the top-left section of the pop-up window, click on the “New Project” option.

• The New Project window appears. Now, you can set the name and the location of the project.

• Then click on the “Create” button and the project will be created.

Step 2: Enable GA4 BigQuery Linking

• Log in to your Google Analytics account. For further information about Google Analytics.
• Click on Google Analytics 4 Admin option, found in the bottom-left corner of the window.
• Now, after going to the GA4 Admin panel, click on “BigQuery Linking”.
• BigQuery Linking window appears. Now, click on the “Link” button beside the search bar.
• “Create a link with BigQuery” window appears. Now, click on the learn more link.
• In the next window, scroll down and copy the Service Account Id of the service account given in point 5 of step 1 (firebase-measurement@system.gserviceaccount.com).
• Now, go back to the “Create a link with BigQuery” window. Then click on the “Choose a BigQuery project” option.
• Then select the name of the BigQuery project that you want to link with Google Analytics 4.
• Now, select the Data Location from the drop-down menu.
• Click on the “Next” button.
• Now, select the type of Data streams. If you have a mobile app and want to export the user ids to Google BigQuery, you may additionally choose “Include advertising Identifiers for mobile app streams.”
• Select the frequency of data movement accordingly i.e, either Daily (once a day) or Streaming (continuous export).
• Now, click on the “Next” button and then review your choices and click on the “Submit” button.
• Now, the link for the GA4 BigQuery is created.

Until now, just the GA4 BigQuery linking is accomplished. But they are still not connected. So, you need to create an API.

Step 3: Enable Google Cloud API

• Go to the Google Cloud Console.
• Then in the left navigation pane, go to API & Services and select “Library”.
• The API Library page appears.

• Now, if you have not selected the project, then click on the current project name at the top. A separate window with the list of projects appears. Select the project you want to link.
• Now, in the search bar, search for BigQuery API and click on it.
• Now, make sure the BigQuery API is enabled and click on the “Manage” button.

Step 4: Add a Service Account

• From the sidebar menu, select “Credentials”.
• Go to “Create Credentials” and then select the “Service account” option.
• In the Service account name, type firebase-measurement@system.gserviceaccount.com, i.e., the Id already copied in step 2. Then in the Service account ID, write the ID where you want to give access to that account and click on “Create”.
• Now, grant the editor access to the Service account. And then click on “Continue” and select “Done”.

Step 5: Use Google BigQuery with GA4 Data

• After all the procedures, wait for 24 hours for the data set to export to your BigQuery project.

• You’ll find 2 tables with each dataset. One for continuous export of raw events throughout the day and another for full daily export of events.
• Now, you can run SQL queries on the tables according to your requirements.

Reasons for linking failures

Linking to BigQuery can fail for either of the following two reasons:

• Your organization’s policy prohibits export to the United States. Choose a different location if you’ve chosen the United States as the location of your data.
• Modify your organization’s policy if your organization policy prohibits service accounts from the domain you want to export data from. 

Reasons for export failures

There are several reasons due to which your GA4 BigQuery Schema Export may fail, such as:

Method 3: Using CSV Files for GA4 BigQuery Integration

This method for integration is useful for one time migrations. Suppose, you have your 100 customer’s data in a google sheet. You don’t have to build data pipelines to move that to BigQuery. CSV is the best option there.

The steps to connect are:

• Log in to your report in Google Analytics, and load the report you want to move to BigQuery. 
• Click on the Share button at the top-right corner of the screen. 
• Select download file and choose CSV as the file format. 
• After downloading the file, you can import or Loading CSV data from Cloud Storage the file into BigQuery using one of the available methods.

Types of GA4 BigQuery exports

• Streaming Export

This export refers to ingesting real-time data to BigQuery for analysis within a few minutes. This is a viable option for businesses that rely on real-time data analysis for their business decisions or require up-to-the-minute data, such as real-time reporting, monitoring user behavior as it happens, and quickly identifying and responding to emerging trends.

• Daily Export

This export refers to exporting a complete data set and transferring it to BigQuery in 24-hour cycles. This method is cost-effective and sufficient for most reporting and analysis needs that do not require real-time data. It allows for comprehensive daily analysis without the higher costs associated with continuous data transfer.

GA4 BigQuery Export Schema

BigQuery provides a schema format for all the data exported from GA4 to BigQuery. When the data is exported from GA4 to BigQuery, a dataset prefixed by ‘analytics_’  is automatically created in BigQuery. 

Each day, a new table containing the previous day’s data is created within this dataset. These tables are named events_YYYYMMDD, followed by the date they represent. 

For example, the table for October 15th 2023, would be named events_20231015.

Those who have activated the Streaming export feature will notice an additional table labeled events_intraday _YYYYMMDD. This table is updated in near real-time and replaced daily with a new one.

In the BigQuery interface, these individual tables are displayed collectively under a single name, simplifying the visual representation.

Which of these methods allows you to load GA4 historical data to BigQuery?

• You can use Hevo’s automated data pipeline platform or Google Analytics API to connect to BigQuery.
• You can get into the user level detail with GA API. But, it requires more steps to extract and load the data. As Hevo’s pipelines are automated, the effort and time will be much lesser.
• It also gives you the flexibility to decide the period of historic load based on your use case. And, moving the historical data is free of cost.

Which Google Analytics properties data can you export to BigQuery?

• A property in Google Analytics implies a website, blog, or application having a distinct tracking ID. In your GA account. You can decide the number of properties based on your use case.. 
• Using the above methods, you can export the details of these properties to your BigQuery.
• While configuring your source using Hevo Data, you will have the option to select your property.

After you’ve exported Google Analytics data to BigQuery, what can you achieve with the data?

By migrating your data from GA4 to BigQuery, you will be able to help your business stakeholders find the answers to these questions:

• Which Demographic contributes to the highest fraction of users of a particular Product Feature? 
• How are Paid Sessions and Goal Conversion Rate varying with Marketing Spend and Cash in-flow?
• How to identify your most valuable customer segments?

Why should you enable the BigQuery linking for GA4?

There are several reasons to allow BigQuery linking for GA4, such as: 

• To store your data in BigQuery (Google Cloud) and/or send it to your data warehouse in other clouds like Azure or Snowflake
• To join and enrich your data with other marketing or contextual data
• To visualize your data in tools like Tableau or PowerBI
• To perform advanced analysis
• To use your data as input for (machine learning) models

Additional Resources on GA4 BigQuery Integration

• Explore more on Bigquery to Bigquery Migration
• Export Google Analytics Data

Key Takeaways

This article has discussed 3 methods for setting up GA4 BigQuery Integration.

• If you can take all the responsibility for implementing this integration, you can continue with the manual method.
• However, if you want a more seamless integration that is fully automated and completely managed, you should definitely give Hevo a try.

FAQ on Integrate Data from GA4 to BigQuery