Google Analytics 4

Patterna reads GA4 data from the BigQuery export, never from the GA4 API. You link GA4 to BigQuery, give a service account read access to the export dataset, and the jobs read new days on a schedule.

1Link GA4 to BigQuery#

1. In GA4, open Admin → Product links → BigQuery links and choose Link.
2. Pick the Google Cloud project that will hold the export.
3. Choose a data location in the EU.
4. Turn on the Daily export. Patterna does not read the intraday (streaming) table yet.
5. Submit. The first daily table, events_YYYYMMDD, appears in a dataset named analytics_<property id> within about a day.

2Give the reader access#

Grant Patterna's reader service account read access. Its address is shown on Connectors → Google Analytics 4.

1. In Google Cloud, open BigQuery, select the analytics_<property id> dataset and choose Sharing → Permissions.
2. Add the service account with the role BigQuery Data Viewer. This lets it read the tables and nothing else.
3. To run queries, the service account also needs BigQuery Job User on the project you name in the connector. The query jobs run in that project, so it carries the (small) query cost.

Patterna supplies the service account; the jobs run with its credentials. You never upload a key.

3Turn on the connector#

In Connectors → Google Analytics 4, fill in the project and dataset the card asks for and press Test connection. The connector runs on the connectors schedule: the scheduler checks every 15 minutes and runs it when your tenant's interval has passed.

What the reader does#

• Reads new daily tables with a query, row by row through the same mapping rules as PostHog.
• user_pseudo_id is a device id. It is used only as an HMAC input to derive session and event ids. It is never stored.
• GA4's user_id, when you set one, becomes the Patterna user id. Set it to your random Patterna id, never your account id; or turn on id_mapping: "hmac" on the connector so it is replaced by a Patterna-minted keyed hash (under PATTERNA_ID_SECRET, which Patterna holds) before it is written. See Identity across connectors.
• Event ids are derived from the source, so re-reading a day does not double count. Ingest is idempotent on event_id.
• Users who are non_profiled get a per-day rotating session_id instead of a user id.
• Each run writes a row with rows in, rows mapped and per-field drop counts.

What is dropped#

• Every user property and device field except OS, app version and locale.
• IP-derived geography and any coordinates.
• Free text such as page titles and search terms.
• Event types without a mapping.

The rules are the same as for PostHog: see Data we never store.

Check it works#

Connectors → Google Analytics 4 shows the last run, rows in, rows mapped, fields dropped and errors. A failing run shows its error and the request id.