Google Analytics 4 & BigQuery Integration: The Complete Guide

Google Analytics 4 (GA4) offers powerful built-in reports, but there are times when you need unsampled raw data, advanced segmentation, or cross-platform analysis that GA4’s interface can’t provide. This is where BigQuery, Google’s cloud-based data warehouse, becomes essential.

By integrating GA4 with BigQuery, you can store, query, and analyze every event your users generate, without sampling limitations. As Mr. Phalla Plang, Digital Marketing Specialist, I tell clients: “GA4 gives you the story, but BigQuery gives you every word of the book.”

1. Why Connect GA4 to BigQuery

GA4’s reports aggregate and sometimes sample data, especially for large datasets or long date ranges. BigQuery solves this by:

• Providing raw, unsampled event data for complete accuracy.
  
• Allowing custom SQL queries for advanced analysis.
  
• Combining analytics data with other sources, such as CRM or advertising platforms.
  
• Extending data retention beyond GA4’s limits—GA4 keeps most data for a maximum of 14 months, but BigQuery can store it indefinitely (Google Support, n.d.-a).
  

This means you can build detailed customer journeys, lifetime value reports, and predictive models using tools like Looker Studio.

2. BigQuery Access in GA4 vs Universal Analytics

In Universal Analytics (UA), BigQuery export was available only to Google Analytics 360 (paid) users. With GA4, the BigQuery integration is free for all properties (Wikipedia, 2025).

You only pay for BigQuery storage and query processing, with a generous free tier from Google Cloud (Google Cloud, n.d.).

3. BigQuery Free Tier Pricing

As of 2025, BigQuery’s free tier includes:

• 10 GB of storage per month.
  
• 1 TB of query processing per month (Google Cloud, n.d.).
  

Most small to medium-sized businesses never exceed these limits, making GA4–BigQuery integration essentially free for them.

4. How GA4 Exports Data to BigQuery

When linked, GA4 exports:

• Daily tables – All events from the previous day.
  
• Streaming tables – Near real-time event data, updated every few minutes (Google Support, n.d.-b).
  

Each exported record includes:

• Event name (page_view, purchase, etc.)
  
• Event parameters (e.g., transaction_id, search_term)
  
• User properties (e.g., country, device_category)
  

5. Step-by-Step: Linking GA4 to BigQuery

Step 1: Create a BigQuery Project

• Go to the Google Cloud Console.
  
• Create a new project or select an existing one.
  

Step 2: Enable BigQuery API

• In Cloud Console, navigate to APIs & Services.
  
• Enable the BigQuery API.
  

Step 3: Link from GA4

1. In GA4, go to Admin → BigQuery Links.
   
2. Click Link and choose your BigQuery project.
   
3. Select Daily or Streaming export.
   
4. Save your settings (Google Support, n.d.-b).
   

Step 4: Verify Data in BigQuery

• Open BigQuery and look for a dataset named analytics_<property_id>.
  
• Inside, you’ll find event tables labeled by date.
  

6. Common Use Cases for GA4 & BigQuery

1. Extended Data Retention – Keep years of analytics data for historical comparisons.
   
2. Advanced Segmentation – Build highly specific audience segments beyond GA4’s UI limits.
   
3. E-commerce Funnel Analysis – Identify drop-off points at checkout.
   
4. Cross-Channel Data Blending – Combine GA4 data with ads, email, or sales data for full-funnel analysis.
   
5. Machine Learning Models – Train models to predict churn, lifetime value, or conversion likelihood.
   

7. Example Query: Top Site Search Terms

SELECT

  ep.value.string_value AS search_term,

  COUNT(*) AS searches

FROM

  `project_id.analytics_123456789.events_*`,

  UNNEST(event_params) AS ep

WHERE

  event_name = ‘view_search_results’

  AND ep.key = ‘search_term’

GROUP BY search_term

ORDER BY searches DESC

(BigQuery Documentation, n.d.)

This query returns the most common search terms on your site.

8. Best Practices for GA4–BigQuery Integration

• Use partitioned tables to reduce query costs and improve speed.
  
• Schedule queries for automated reporting.
  
• Filter unnecessary data before storing it long-term.
  
• Leverage Looker Studio to turn queries into shareable dashboards.
  
• Document your data schema to make team usage easier.
  

9. Pitfalls to Avoid

• Uncontrolled storage growth – Archive or delete old datasets you don’t need.
  
• Complex SQL without documentation – Keep reusable queries stored for future use.
  
• Privacy compliance issues – Anonymize personal data if required by regulations.
  

10. Real-World Example: Retail Brand Success

A retail company linked GA4 to BigQuery and:

1. Combined on-site behavior data with CRM purchase history.
   
2. Identified high-value repeat customers.
   
3. Created remarketing lists targeting similar users.
   

The result was a 15% higher ROI on paid ads after audience adjustments.

11. Summary Table: GA4 & BigQuery Setup

Step	Action
Create BigQuery Project	In Google Cloud Console
Enable API	BigQuery API in APIs & Services
Link from GA4	Admin → BigQuery Links
Choose Export	Daily or Streaming
Verify Data	Check dataset in BigQuery

References

BigQuery Documentation. (n.d.). Querying event data from Google Analytics 4. Retrieved August 10, 2025, from https://cloud.google.com/bigquery/docs

Google Cloud. (n.d.). BigQuery free tier. Retrieved August 10, 2025, from https://cloud.google.com/bigquery/pricing

Google Support. (n.d.-a). Data retention in Google Analytics 4. Retrieved August 10, 2025, from https://support.google.com/analytics/answer/7667196?hl=en

Google Support. (n.d.-b). Link your Google Analytics property to BigQuery. Retrieved August 10, 2025, from https://support.google.com/analytics/answer/9823238?hl=en

Wikipedia. (2025). Google Analytics. In Wikipedia. Retrieved August 10, 2025, from https://en.wikipedia.org/wiki/Google_Analytics