Ecommerce reporting without a connector subscription

You can build a weekly ecommerce report from GA4 and Shopify data without a paid connector. Google calls Data Studio, named Looker Studio until April 2026, "a no-cost tool", and GA4 exports raw events to BigQuery. The free route has three gaps: Google's own connector list has no Shopify or Meta connector, the BigQuery sandbox deletes tables after 60 days by default, and Shopify's orders CSV has no campaign column. Close those and the route answers what happened, not what caused it.

What does each free piece cover?

Four pieces, each from its own documentation:

1. GA4 to BigQuery. The BigQuery export sends raw events, purchases included, to your own project. Standard properties have a limit of "1 million events for Daily (batch) exports", per the setup page, and "Once you export data from Analytics to BigQuery, you cannot re-export it."
2. Data Studio. Google says you can use "free connectors built by Google" for Google Sheets, Google Ads, Google Analytics and other Google Marketing Platform products (about data sources). Its connector catalog lists GA4, BigQuery, Google Ads, Google Sheets and CSV upload. The GA4 connector is "subject to Google Analytics Data API (GA4) quotas", and GA4 segments and comparisons are not available in it.
3. Shopify's orders CSV. The Orders export writes order name, date, totals, refunds and shipping province. Its column list has no UTM, referrer or campaign field, so an order's channel has to come from GA4.
4. Ad spend. Google Ads has a free connector. For other platforms, Data Studio points to "connectors built by 3rd party developers", such as "Bing, Facebook Ads, and Salesforce" (about connectors), and warns that "Community connectors may cost money to use". Without one, the route is a file: the catalog offers "Upload CSV files".

Where does the free route break?

Three documented limits:

1. The sandbox forgets. The BigQuery sandbox grants "a lifetime limit of 10 GiB of storage" and "1 TiB of processed query data each month", and "all tables, views, and partitions automatically expire after 60 days". To keep a year of history, Google's upgrade steps are to enable billing, then update the default expiration. GA4's help says exports that exceed the sandbox limits incur charges.
2. A missed day stays missed. If an export runs over its limit, "previous days' exports will not be reprocessed". The help pages say data should start "flowing" within 24 hours of linking and mention no backfill of earlier days, so link GA4 now, even if you build the report later.
3. Totals differ. Google says BigQuery export data "might differ from the data in the Google Analytics interface". Expect gaps against GA4's reports and against Shopify.

When is a paid connector worth it?

A connector automates the pulls on a schedule. Supermetrics, a marketing data pipeline tool, lists plans from $44 a month billed yearly, or $55 billed monthly, with extra destinations and users priced separately (as read on 8 September 2026 on its pricing page). The free route costs time instead: a weekly export for each platform Google has no connector for. A connector is worth its price when several platforms must land in one sheet every week and the exports cost you more than the fee. Neither route says what caused the sales; the Supermetrics comparison covers that split.

How do you build the weekly report in an afternoon?

Five steps, each with a pass and a fail:

1. Link GA4 to BigQuery. In GA4 Admin, under Product links, open BigQuery links, link a project and choose the Daily export. Pass: a dataset with daily tables appears within 24 hours. Fail: nothing after 24 hours, so read Google's list of reasons for linking failures.
2. Size your events. Find your busiest day's event count in GA4's Events report. Pass: well under 1 million. Fail: near or over it, so exclude streams or events in the link settings, as Google's help advises.
3. Set the expiry before the sandbox does. If you use the sandbox, read the dataset's default table expiration. Pass: you chose the period. Fail: the 60-day default is still there.
4. Export Shopify orders for the same weeks. Open Orders, then Export, then Orders by date, and upload the CSV to Data Studio or a Google Sheet.
5. Reconcile one week. Run the query below, then put Shopify's order count beside GA4's purchases for each day. Pass: each day's gap has a cause you can name, such as canceled or test orders, blocked tracking, or a different ID sent as transaction_id. Fail: a gap that moves day to day with no cause, which points to a tag problem. Why Google Ads and Shopify revenue differ lists causes.

SELECT
  PARSE_DATE('%Y%m%d', event_date) AS day,
  COUNT(DISTINCT ecommerce.transaction_id) AS purchases,
  SUM(ecommerce.purchase_revenue) AS revenue
FROM `your-project.analytics_123456789.events_*`
WHERE event_name = 'purchase'
  AND _TABLE_SUFFIX BETWEEN '20260921' AND '20260927'
GROUP BY day
ORDER BY day;

The field names come from Google's export schema; purchase_revenue is in local currency and event_date is in the property's registered time zone.

Sources, 30 September 2026: BigQuery Export and Set up BigQuery Export (Google Analytics Help); BigQuery export schema (Google Analytics Help); Try BigQuery using the sandbox (Google Cloud, last updated 24 September 2026); Data Studio documentation (welcome, available connectors, Google Analytics connector, about connectors, about data sources; Google Cloud, last updated 24 September 2026); Exporting orders (Shopify Help Center); Supermetrics pricing (vendor page, as read on 8 September 2026).

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