GA4 BigQuery: How to Integrate GA4 with BigQuery

GA4 BigQuery: How to Integrate GA4 with BigQuery

Introduction to GA4 and BigQuery

Google Analytics 4 (GA4) is a powerful analytics platform, but its native reporting interface has limitations, especially when analyzing large datasets. Integrating GA4 with BigQuery allows businesses to access raw event-level data, enabling deeper analysis, custom reporting, and advanced insights.

Why GA4 and BigQuery Integration is Essential

  • GA4 provides event-based tracking, but the GA4 interface has limited data retention (2-14 months).
  • BigQuery allows long-term storage of GA4 data for historical analysis.
  • Enables advanced custom queries that aren’t possible in GA4’s UI.
  • Supports integration with BI tools like Looker, Power BI, and Data Studio.

Benefits of Using BigQuery for GA4 Data

  • Scalability: Handles large datasets efficiently.
  • Customization: Write SQL queries for deep data exploration.
  • Automation: Schedule reports and integrate with other Google Cloud services.
  • Cost Efficiency: Pay only for the queries you run, reducing storage costs.

Setting Up BigQuery for GA4

Prerequisites

Before linking GA4 with BigQuery, ensure you have:
✅ A Google Cloud Platform (GCP) account with billing enabled.
✅ Admin access to your GA4 property.
✅ A BigQuery project created in Google Cloud.

connect ga4 and bigquery

Enabling BigQuery Export in GA4

  1. Log in to Google Analytics and go to Admin.
  2. Under Property Settings, select BigQuery Linking.
  3. Click Link, then select your Google Cloud Project.
  4. Choose your data region (e.g., US, EU) for storage.
  5. Select Daily export or Streaming export (or both).
  6. Click Save to complete the setup.

Connecting GA4 with BigQuery

Step-by-Step Guide to Linking GA4 to BigQuery

  1. Open Google Cloud Console.
  2. Navigate to BigQuery and select your project.
  3. Confirm that GA4 data appears under analytics_XXXXXX datasets.
  4. Click on Tables (e.g., events_YYYYMMDD) to view event-level data.

Configuring Data Export Settings

  • Daily Export: Stores data once per day.
  • Streaming Export: Sends data in real time (useful for live tracking).
  • Data Retention: Adjust settings to match business needs (default is 2 months in GA4).

Understanding GA4 Data in BigQuery

Key Tables in BigQuery Export

  • events_YYYYMMDD: Contains user interactions (pageviews, clicks, purchases).
  • event_params: Stores additional details about events (e.g., product_id, transaction_value).
  • user_properties: Holds user-level attributes (e.g., device, location).

Understanding Event-Based Data Storage

GA4 uses an event-driven model, meaning data is structured around user interactions instead of sessions. Unlike Universal Analytics, GA4 does not have predefined categories (e.g., Pageviews, Transactions), so all user actions are logged as events.

Writing SQL Queries to Analyze GA4 Data

Basic SQL Queries to Retrieve User Data

SELECT event_name, COUNT(*) AS event_count
FROM `my_project.analytics_XXXXXX.events_*`
WHERE _TABLE_SUFFIX BETWEEN '20240301' AND '20240307'
GROUP BY event_name
ORDER BY event_count DESC

➡ This query retrieves the most common user interactions over the past 7 days.

Filtering by Events, User Properties, and Session Data

SELECT user_pseudo_id, event_name, event_bundle_sequence_id
FROM `my_project.analytics_XXXXXX.events_*`
WHERE event_name = 'purchase'

➡ This query extracts purchase events, showing user IDs and event sequences.

Using Joins to Combine GA4 Tables for Deeper Insights

SELECT u.user_pseudo_id, e.event_name, e.event_timestamp
FROM `my_project.analytics_XXXXXX.events_*` e
JOIN `my_project.analytics_XXXXXX.user_properties_*` u
ON e.user_pseudo_id = u.user_pseudo_id
WHERE e.event_name = 'page_view'

➡ This joins event data with user properties to get user-level insights.

Advanced Analysis with BigQuery

Creating Custom Reports for User Behavior

  • Track time spent on pages by calculating event_timestamp differences.
  • Identify top entry and exit pages for better UX improvements.

Identifying Top-Performing Traffic Sources

SELECT traffic_source.source, COUNT(*) AS session_count
FROM `my_project.analytics_XXXXXX.events_*`
GROUP BY traffic_source.source
ORDER BY session_count DESC

➡ This query finds which traffic sources drive the most sessions.

Analyzing Conversion Paths and User Journeys

  • Use sequence analysis to track user flow from landing page → conversion event.
  • Identify drop-off points in the customer journey.

Automating Reports and Dashboards

Exporting BigQuery Data to Google Data Studio

  1. Open Google Data Studio.
  2. Select BigQuery as a data source.
  3. Connect your GA4 dataset.
  4. Build dashboards for real-time insights.

Scheduling Queries and Automating Data Refresh

  • Use Cloud Scheduler to automate BigQuery queries.
  • Send reports to Google Sheets or Looker for easier analysis.

Using BI Tools (Looker, Power BI) for Visualization

  • Connect BigQuery to Looker Studio for real-time dashboards.
  • Integrate with Power BI for deeper segmentation and filtering.

Best Practices and Optimization

Managing BigQuery Costs with Partitioning and Clustering

  • Use date partitioning to avoid querying unnecessary data.
  • Cluster tables based on event_name to speed up queries.

Optimizing Query Performance

  • Use SELECT only required columns instead of SELECT *.
  • Use preview mode before running full queries.
  • Aggregate data into summary tables for faster reporting.

Ensuring Data Accuracy and Privacy Compliance

  • Apply GA4 data retention policies correctly.
  • Use Google Cloud IAM roles to control data access.