How do I use BigQuery with GA4 for advanced analysis?

Applies to: Google Analytics 4 (GA4), Google BigQuery
Last updated: May 2025

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Problem

You need access to raw GA4 data for advanced queries, custom reports, or large-scale analysis beyond what GA4’s UI and Explorations can provide.

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Solution

GA4 supports native integration with Google BigQuery, allowing you to export all event-level data into a warehouse environment. From there, you can write SQL queries, build dashboards, or join with other datasets.

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Step-by-Step Guide

Step 1: Set Up a Google Cloud Project

1. Go to https://console.cloud.google.com
2. Create or select a Google Cloud project
3. Enable BigQuery API under APIs & Services
4. Ensure billing is enabled for the project

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Step 2: Link GA4 to BigQuery

1. In https://analytics.google.com, go to your GA4 property
2. Click Admin > BigQuery Links
3. Click Link
4. Select your Cloud project
5. Choose:
   • Daily export (once per day)
   • Streaming export (export data in near real-time; optional, adds cost)
6. Review and submit

GA4 will start exporting your data to a dataset named analytics_<property_id> in BigQuery.

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Step 3: Explore GA4 Data in BigQuery

Once data starts flowing, use the BigQuery console to run SQL queries.

Example: Top pages by user count

SELECT
  event_params.value.string_value AS page_path,
  COUNT(DISTINCT user_pseudo_id) AS users
FROM
  `your_project.analytics_123456789.events_*`,
  UNNEST(event_params) AS event_params
WHERE
  event_name = "page_view"
  AND event_params.key = "page_location"
GROUP BY page_path
ORDER BY users DESC
LIMIT 10

You can also analyze:

• User journeys
• Funnel drop-offs
• Custom conversions
• Event sequencing
• Join with CRM or ad data

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Step 4: Visualize BigQuery Data (Optional)

You can connect BigQuery to tools like:

• Looker Studio
• Power BI
• Tableau
• Google Sheets

Use them to create custom dashboards or reports on top of your raw GA4 data.

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Notes

• GA4’s BigQuery export includes every user interaction (event-level data)
• Data is structured by:
  • events_* tables (daily)
  • Fields like event_name, user_pseudo_id, event_params, user_properties
• BigQuery costs are based on storage and query size—be cautious with large datasets