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Square to Snowflake integration for analytics-ready sales data

The Square to Snowflake integration from Adapters loads payments, orders, line items, refunds, and processing fees from the Square API into Snowflake tables on an incremental schedule, so sales, margin, and reconciliation models read a full history across every location instead of exporting reports by hand. Field mapping is no-code.

Field mapping auto-plugged · tap a port to rewire

5 sample records ready

Last updated July 2026

What running Square to Snowflake by hand costs you

  • Square scopes payments and orders per location and paginates by cursor, so pulling a full multi-location history into a warehouse means chaining many throttled API calls.
  • Processing fees and refunds arrive as separate objects, and tying them back to the original payment in a spreadsheet is slow and easy to miscount.
  • Comparing Square in-store sales against online channels and ad spend needs one warehouse table, not a stack of per-location CSV exports.

The field mapping, out of the box

These cables are pre-wired when you pick the pair. Rewire any of them, or add your own, in the same visual data mapping tool you use for every adapter.

Input / SQUARE

payment.id
FCT_PAYMENT.payment_id
payment.amount_money.amount
FCT_PAYMENT.amount
payment.created_at
FCT_PAYMENT.created_at
payment.processing_fee.amount_money
FCT_PAYMENT.fee
payment.location_id
DIM_LOCATION.location_id
payment.updated_at
FCT_PAYMENT.loaded_at

Transforms included

Incremental loads use the Square updated_at watermark per location so only new and changed payments come through each run; minor-unit amounts (cents) cast to Snowflake NUMBER dollars, ISO 8601 timestamps become TIMESTAMP_NTZ in UTC, and processing fees and refunds join back to their payment so gross, fee, and net models read one deduplicated table.

How it goes live

Three steps, minutes end to end, covered by flat data integration pricing from $49 a month.

STEP 01

Pick the pair

Connect Square and Snowflake with scoped credentials. About a minute each.

STEP 02

Confirm the mapping

The cables above are pre-wired. Adjust any field, preview the transform on sample records, done.

STEP 03

Schedule the sync

Hourly down to every minute, with retries, alerting, and a full log on every run.

Try it in the live demo Preloads SQUARE → SNOWFLAKE with sample records

Square to Snowflake sync: common questions

How does the Square to Snowflake sync work?

The Square to Snowflake integration from Adapters loads payments, orders, line items, refunds, and processing fees from the Square API into Snowflake tables on an incremental schedule, so sales, margin, and reconciliation models read a full history across every location instead of exporting reports by hand. Field mapping is no-code.

How much does the Square Snowflake integration cost?

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.

How often can Adapters sync Square to Snowflake?

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.

Do I need to write code to connect Square and Snowflake?

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 per location so only new and changed payments come through each run; minor-unit amounts (cents) cast to Snowflake NUMBER dollars, ISO 8601 timestamps become TIMESTAMP_NTZ in UTC, and processing fees and refunds join back to their payment so gross, fee, and net models read one deduplicated table.

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Browse the full api connector library, or request a pair you do not see.

Square and Snowflake, finally in agreement

Map the pair once and let it sync on schedule. Flat price from $49 a month, no per-task fees.

Try the live demo

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