On April 29, 2025, Google launched a significant update to the GA4 to BigQuery export schema. This new structure introduces a modular export format that simplifies query operations, enhances scalability, and supports modern data practices.

If you're working with GA4 and BigQuery, here's a breakdown of what's changed, why it matters, and how you can leverage it.

What’s New in the GA4 to BigQuery Export Schema?

Previously, GA4 exported raw data into complex tables like events_* and users_*, packed with deeply nested fields (such as event_params, user_properties, and items). Querying this data often required the use of UNNEST and extensive SQL logic.

Now, Google has introduced a modular and semi-processed export model, with tables separated by data domain for easier access and optimized performance.

New Table Mapping Overview

To ensure a smooth transition, Google has also introduced standardized views like ga4_Events and ga4_UserAcquisition, preserving compatibility with the previous structure.

Why This Modular Schema Matters

1. Reduced Complexity

The new schema eliminates the need for repeated UNNEST functions, allowing analysts to write straightforward queries. This leads to faster development and greater analytical agility.

2. Improved Performance and Cost Efficiency

BigQuery now scans only relevant data thanks to the segmented structure. This results in quicker query processing and lower costs, especially for frequent or large-scale data operations.

3. Enhanced Data Governance

With clear separation by data domain, organizations can apply more precise access controls, conduct audits efficiently, and track data changes with better granularity.

4. Ready for Modern Data Architectures

The modular format supports smoother integrations with ETL pipelines, business intelligence tools like Looker and Power BI, and machine learning models, making it ideal for scalable data ecosystems.

5. Early Access Required

At this time, the modular export schema is not yet available by default. You need to contact your Google account representative to request allowlist access for this new format.

What This Means for Data Teams

This update presents a strategic opportunity for teams working with Google Analytics and BigQuery. It’s the perfect time to:

• Review and optimize legacy queries
  
  
  

• Redesign dashboards to leverage the new schema
  
  
  

• Update automated pipelines and scripts
  
  
  

• Strengthen data access controls
  
  
  

• Reduce operational and processing costs
  
  
  

By adopting this model, organizations can enhance data accuracy, maintain compliance, and enable more agile decision-making.

Expert Perspective

As specialists in analytics, tagging, and data architecture, we view this change as a key advancement in digital analytics infrastructure. It brings GA4 closer to best-in-class data warehousing standards and empowers teams to operate with greater clarity and efficiency.

If you're still working with the older schema, this is the right time to modernize your stack and take advantage of this scalable architecture.

Learn More

For technical details and official documentation, visit Google Cloud: GA4 Export Schema Guide.

Frequently Asked Questions

1. What is the new GA4 export schema in BigQuery?

It’s a modular schema where GA4 data is split into purpose-specific tables instead of a single large nested structure.

2. Why is the new schema better?

It simplifies SQL queries, improves performance, and offers better governance and compatibility with modern data tools.

3. Can I use my existing queries with the new schema?

Yes. Google has provided standardized views that maintain compatibility with the old structure.

4. Is this schema available to everyone?

No. You need to request access via your Google representative to enable the new schema export.

5. Will this change affect dashboard tools like Power BI or Looker?

The change enhances compatibility, making it easier to build and maintain dashboards in tools like Looker, Power BI, and others.

6. How do I prepare for migration?

Start by auditing your current pipelines, rewriting complex queries, and designing your workflows to align with the new modular structure.