Ga4 Bigquery Export

GA4 → BigQuery Export

Overview

The Data API is good but bounded — sampled past a threshold, capped at ~150 dimensions, no cohort joins. BigQuery export gives you the raw event stream as SQL-queryable tables, free at the standard GA4 tier (up to 1M events/day), with no sampling and full event payloads.

This skill: one-time setup, then the SQL recipes for what the Data API can't do well.

Prerequisites

• A GA4 property for which you can create a BigQuery link.
• A Google Cloud project with BigQuery enabled and permission to grant project IAM roles.
• The bq and gcloud CLIs authenticated to that project; use the service account created by ga4-auth-setup for unattended queries.

Instructions

Setup — one time

1. Link your GA4 property to a GCP project

In https://analytics.google.com/:

1. Admin → Property column → BigQuery Links
2. Create a link → pick your GCP project
3. Data location: pick the BQ region for the export tables (US multi-region is fine for most cases; EU if you need data residency)
4. Export type:
   • Daily — single events_YYYYMMDD table per day, written ~24h after midnight. Fine for most reporting.
   • Streaming — events_intraday_YYYYMMDD table written ~near-real-time. Costs more, useful for hot ops dashboards.
   • Most setups: enable both. Streaming for "today", daily for everything else.
5. Include advertising identifiers — uncheck unless you specifically need device-graph data (most don't)
6. Save

The first daily table lands within 24h. The first streaming table is near-immediate. After that you have a new events_YYYYMMDD every day forever, no maintenance needed.

2. Verify the export is working

PROJECT=your-gcp-project
DATASET=analytics_123456789       # auto-named after the property ID

bq ls "$PROJECT:$DATASET" 2>&1 | head -10
# Expect: events_YYYYMMDD tables + events_intraday_YYYYMMDD if streaming enabled

If you see no dataset, the link is configured but the first export hasn't fired yet — wait 24h.

3. Authorize a service account for querying

The SA from ga4-auth-setup only has Data API access. For BQ queries, grant the same SA:

SA_EMAIL=ga4-reader@your-project.iam.gserviceaccount.com
gcloud projects add-iam-policy-binding "$PROJECT" \
  --member="serviceAccount:$SA_EMAIL" \
  --role="roles/bigquery.dataViewer"
gcloud projects add-iam-policy-binding "$PROJECT" \
  --member="serviceAccount:$SA_EMAIL" \
  --role="roles/bigquery.jobUser"

dataViewer reads tables; jobUser lets the SA run queries (queries are jobs in BQ's model).

The events table schema (the important columns)

Every row in events_YYYYMMDD is one event. The schema is denormalized — user + session + event + page + device all flat in each row.

The event_params and user_properties arrays are the gnarly bit. Pulling a parameter requires UNNEST + filter. The idiom:

-- Pull the page_location for every page_view
SELECT
  TIMESTAMP_MICROS(event_timestamp) AS ts,
  (SELECT value.string_value FROM UNNEST(event_params) WHERE key = 'page_location') AS page,
  user_pseudo_id
FROM `your-project.analytics_123456789.events_*`
WHERE _TABLE_SUFFIX BETWEEN '20260513' AND '20260520'
  AND event_name = 'page_view'
LIMIT 100;

_TABLE_SUFFIX BETWEEN '...' AND '...' is the canonical way to scan a date range across the wildcard table. Always set it — without a suffix filter, you query the entire history and pay for it.

Examples

Recipe 1 — True cohort retention

The thing the Data API can't do cleanly:

WITH first_seen AS (
  SELECT
    user_pseudo_id,
    DATE(MIN(TIMESTAMP_MICROS(event_timestamp))) AS first_date
  FROM `your-project.analytics_123456789.events_*`
  WHERE _TABLE_SUFFIX BETWEEN '20260401' AND '20260520'
  GROUP BY user_pseudo_id
),
activity AS (
  SELECT
    user_pseudo_id,
    DATE(TIMESTAMP_MICROS(event_timestamp)) AS active_date
  FROM `your-project.analytics_123456789.events_*`
  WHERE _TABLE_SUFFIX BETWEEN '20260401' AND '20260520'
  GROUP BY user_pseudo_id, active_date
)
SELECT
  DATE_TRUNC(f.first_date, WEEK) AS cohort_week,
  DATE_DIFF(a.active_date, f.first_date, WEEK) AS weeks_since,
  COUNT(DISTINCT a.user_pseudo_id) AS active_users
FROM first_seen f
JOIN activity a USING (user_pseudo_id)
GROUP BY cohort_week, weeks_since
ORDER BY cohort_week, weeks_since;

Output: rows of (cohort_week, weeks_since, active_users). Pivot in your tool of choice for the classic triangle chart.

Recipe 2 — Sessions table (denormalized from events)

GA4's BQ export is event-rows, not session-rows. To reason about sessions, build the session table yourself:

WITH sessions AS (
  SELECT
    user_pseudo_id,
    (SELECT value.int_value FROM UNNEST(event_params) WHERE key = 'ga_session_id') AS session_id,
    MIN(TIMESTAMP_MICROS(event_timestamp)) AS session_start,
    MAX(TIMESTAMP_MICROS(event_timestamp)) AS session_end,
    COUNT(*) AS event_count,
    COUNTIF(event_name = 'page_view') AS pageviews,
    ANY_VALUE(device.category) AS device,
    ANY_VALUE(geo.country) AS country,
    ANY_VALUE(session_traffic_source_last_click.manual_campaign.source) AS source,
    ANY_VALUE(session_traffic_source_last_click.manual_campaign.medium) AS medium,
  FROM `your-project.analytics_123456789.events_*`
  WHERE _TABLE_SUFFIX BETWEEN '20260513' AND '20260520'
  GROUP BY user_pseudo_id, session_id
  HAVING session_id IS NOT NULL
)
SELECT * FROM sessions
ORDER BY session_start DESC
LIMIT 100;

You'd usually CREATE TABLE or CREATE MATERIALIZED VIEW over this — querying the events table directly every time is slow + expensive.

Recipe 3 — Top pages by source

SELECT
  (SELECT value.string_value FROM UNNEST(event_params) WHERE key = 'page_location') AS page,
  session_traffic_source_last_click.manual_campaign.source AS source,
  COUNT(*) AS pageviews,
  COUNT(DISTINCT user_pseudo_id) AS users
FROM `your-project.analytics_123456789.events_*`
WHERE _TABLE_SUFFIX BETWEEN '20260513' AND '20260520'
  AND event_name = 'page_view'
GROUP BY page, source
HAVING pageviews > 10        -- filter the long tail
ORDER BY pageviews DESC
LIMIT 50;

Cost considerations

BigQuery costs $5/TB scanned (first 1 TB/month free). What that translates to in practice:

Stay under the free tier with normal usage. Cost-saving patterns:

1. Always set _TABLE_SUFFIX BETWEEN — don't scan all history if you only need 7 days
2. Materialize hot queries — CREATE TABLE / CREATE MATERIALIZED VIEW for sessions, daily aggregates, etc.
3. SELECT only the columns you need — BQ is columnar; selecting event_params array always reads the whole array even if you only want one parameter
4. Use --dry-run before any new query to see TB scanned: bq query --dry_run --use_legacy_sql=false "SELECT ..."

Streaming vs daily — which to query

Today's data lives in events_intraday_TODAY; tomorrow it gets rolled into events_TODAY and the intraday table for today is dropped. So if you have a query that needs both stable history + today, use events_* and filter on _TABLE_SUFFIX.

Output

After the first export window, the linked dataset contains daily events_YYYYMMDD tables (and optional intraday tables). The example queries return cohort, session, or page-level rows suitable for further analysis; every wildcard query includes a bounded _TABLE_SUFFIX range.

Error Handling

Resources

• ga4-auth-setup — for the service account that queries BQ
• ga4-data-api-query — when you DON'T need event-level granularity (Data API is faster + cheaper for aggregates)
• ga4-common-reports — the Data API recipes that BQ supersedes