Square to BigQuery integration for analytics-ready sales data

Through the Square API rather than a direct link, because BigQuery cannot query Square. A connector authenticates with OAuth, reads payments, orders, line items, refunds, customers and settlements, then loads them into partitioned BigQuery tables and merges on the Square object ID.

Partition on the transaction or created date and cluster on location ID. Retail queries almost always filter by date range and store, so that combination cuts the bytes scanned dramatically. Since BigQuery bills on bytes processed, partitioning here has a direct and visible effect on the monthly bill.

Load settlements alongside payments. A Square deposit is the net of gross sales, processing fees, refunds and adjustments in the settlement period, so reconciling against payments alone never balances. With both tables loaded, a settlement joins to its component payments and the bank line ties out.

Hourly covers most retail reporting, and daily is enough if the only consumer is a next morning dashboard. Sub hourly is rarely worth the API traffic unless something operational depends on it, because Square data settles over hours anyway as tips, refunds and adjustments land.

The Square to BigQuery integration from Adapters loads payments, orders, line items, refunds, and processing fees from the Square API into partitioned BigQuery tables on an incremental schedule, so cohort, margin, and reconciliation models run on a full multi-location history instead of a manual export. Field mapping takes about a minute and no code is required.

Yes. This Square to BigQuery connector ships prebuilt: the field mapping is wired the moment you pick the pair, transforms are included, and the first sync can run within minutes. No code or engineering sprint required.

Pricing is flat and monthly: Starter at $49, Growth at $149, Scale at $399. Every plan includes this pair, visual field mapping, and per-record logs. There are no per-task or per-row fees, so the bill stays the same as volume grows.

Hourly on Starter, every 5 minutes on Growth, and down to every minute on Scale. Failed records retry automatically with backoff, and alerting plus a full per-record log come standard on every run.

No. Fields are auto-mapped the moment you pick the pair, and you can rewire any mapping visually before the first sync. Incremental loads use the Square updated_at watermark so edits, refunds, and voids flow through on the next run; minor-unit amounts (cents) cast to BigQuery NUMERIC dollars, ISO 8601 timestamps land as TIMESTAMP in UTC, tables partition by payment date, and line items land in their own table so margin and cohort models read one clean, deduplicated sales history.