The events_ table family contains obfuscated Google Analytics 4 (GA4) event-level export data from the Google Merchandise Store.[ga4-export-docs] It is structured as a series of daily sharded tables, starting from events_20201101 through events_20210131.[metadata] Each row represents a single event (e.g., page_view, scroll, session_start, view_item, purchase) triggered by a user's interaction with the online storefront.

The data is useful for behavioral analysis, funnel conversion mapping, and e-commerce tracking. Key user attributes such as geographic location, device platform, and acquisition traffic source are nested within top-level records. Additionally, parameters associated with specific events or products are stored in repeated record fields (event_params and items), requiring flatting or unnesting operations during querying.

Schema

Below is the flattened schema representation for the sharded daily events table.

Common query patterns

1. Count events and active users by event name

This query counts the total events logged and counts distinct users (user_pseudo_id) for each event type over the full range of tables.

SELECT
  event_name,
  COUNT(1) AS event_count,
  COUNT(DISTINCT user_pseudo_id) AS unique_users
FROM
  `bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*`
WHERE
  _TABLE_SUFFIX BETWEEN '20201101' AND '20210131'
GROUP BY
  1
ORDER BY
  event_count DESC;

2. Extract nested page_location from event_params

Since event_params is a repeated record (ARRAY), you must unnest it or filter using a subquery to extract a specific parameter like page_location for page views.

SELECT
  event_date,
  (SELECT value.string_value FROM UNNEST(event_params) WHERE key = 'page_location') AS page_path,
  COUNT(1) AS page_views
FROM
  `bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*`
WHERE
  event_name = 'page_view'
  AND _TABLE_SUFFIX BETWEEN '20210101' AND '20210115'
GROUP BY
  1, 2
ORDER BY
  page_views DESC;

3. Compute top selling products from items array

To analyze product sales, we unnest the repeated items record structure on purchase events and aggregate quantities.

SELECT
  item.item_id,
  item.item_name,
  SUM(item.quantity) AS units_sold,
  ROUND(SUM(item.item_revenue_in_usd), 2) AS total_revenue_usd
FROM
  `bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*`,
  UNNEST(items) AS item
WHERE
  event_name = 'purchase'
  AND _TABLE_SUFFIX BETWEEN '20201101' AND '20210131'
GROUP BY
  1, 2
ORDER BY
  total_revenue_usd DESC
LIMIT 10;

Metrics

The following predefined and custom audience cohort metrics can be derived from the events log table:

• Purchasers — Users who have logged either in_app_purchase or purchase.
• N-Day Active Users — Users who have logged at least one event with engagement_time_msec > 0 in the last N days.
• N-Day Inactive Users — Active users from the last M days who have not logged any event with engagement_time_msec > 0 in the last N days (M > N).
• Frequently Active Users — Users who have logged at least one event with engagement_time_msec > 0 on N of the last M days.
• Highly Active Users — Users who have been active/engaged for more than N minutes in the last M days.
• Acquired Users — Users acquired via a specific campaign source, medium, and name.
• Google Acquired Cohorts — Users acquired in a specific weekly cohort filtered by Google campaign source.

[ga4-export-docs]: Google Analytics Help: BigQuery Export Schema [metadata]: Source dataset ga4_obfuscated_sample_ecommerce table list metadata.