Square connector
Set up the Square connector in Kaivo: authentication, configuration, the 17 BigQuery tables it syncs, and answers to common questions.
Written By Lauri Raivio
Last updated 15 days ago
Kaivo is a fully managed data platform that syncs your Square data into a Google BigQuery warehouse and keeps it up to date automatically. There is no pipeline to build and no infrastructure to run, so you can spend your time analysing your payment and finance data instead of moving it.
What is the Square connector
Sync your Square sales data into BigQuery with Kaivo to analyse payments, orders, and customers across your locations.
Getting started with the Square connector
- Sign up for Kaivo and create a workspace.
- Connect your Square account.
- Choose which tables to sync.
- Wait for the initial sync to finish.
- Query your data in BigQuery or your favourite AI or BI tool.
Authenticating Square
Authenticate with your API Key.
Configuring the Square connector
When you set up the connector, you provide:
Tables and columns synced from Square
Kaivo syncs 17 tables from Square into a dedicated dataset in your BigQuery warehouse. Click any table to see its columns and types.
bank_accounts (15 columns)
bank_accounts (15 columns)
cash_drawers (14 columns)
cash_drawers (14 columns)
categories (9 columns)
categories (9 columns)
customers (20 columns)
customers (20 columns)
Subtable: customers__cards
Subtable: customers__group_ids
Subtable: customers__segment_ids
discounts (16 columns)
discounts (16 columns)
Subtable: discounts__present_at_location_ids
inventory (8 columns)
inventory (8 columns)
items (16 columns)
items (16 columns)
Subtable: items__item_data__item_options
Subtable: items__item_data__modifier_list_info
Subtable: items__item_data__tax_ids
Subtable: items__item_data__variations
Subtable: items__item_data__variations__item_variation_data__item_option_values
Subtable: items__item_data__variations__item_variation_data__location_overrides
Subtable: items__item_data__variations__present_at_location_ids
Subtable: items__present_at_location_ids
locations (26 columns)
locations (26 columns)
Subtable: locations__capabilities
loyalty (12 columns)
loyalty (12 columns)
modifier_list (10 columns)
modifier_list (10 columns)
Subtable: modifier_list__modifier_list_data__modifiers
orders (41 columns)
orders (41 columns)
Subtable: orders__discounts
Subtable: orders__fulfillments
Subtable: orders__line_items
Subtable: orders__line_items__applied_discounts
Subtable: orders__line_items__applied_taxes
Subtable: orders__line_items__modifiers
Subtable: orders__refunds
Subtable: orders__returns
Subtable: orders__returns__return_line_items
Subtable: orders__service_charges
Subtable: orders__taxes
Subtable: orders__tenders
payments (44 columns)
payments (44 columns)
Subtable: payments__processing_fee
Subtable: payments__refund_ids
refunds (12 columns)
refunds (12 columns)
Subtable: refunds__processing_fee
shifts (16 columns)
shifts (16 columns)
Subtable: shifts__breaks
taxes (16 columns)
taxes (16 columns)
Subtable: taxes__absent_at_location_ids
team_member_wages (7 columns)
team_member_wages (7 columns)
team_members (13 columns)
team_members (13 columns)
How the Square sync works
After the first load, Kaivo keeps your BigQuery warehouse up to date for you. Where Square supports it, each sync pulls only new and changed records so it stays fast; otherwise it refreshes the whole table. Every record keeps its original ID, so you won't get duplicate rows.
Frequently asked questions
How long does the initial sync take for Square?
It depends on how much history is in your Square account. Most initial syncs finish within minutes, while large accounts can take a few hours. After that, syncs only fetch new and changed records, so they're much faster.
Can I sync only some tables or columns?
Yes. You pick which tables to sync when you set up the connection and can change the selection later. Tables you don't select are never copied to your warehouse.
What happens when Square's schema changes?
New fields are never added automatically. You choose which fields to sync, so data you haven't selected (sensitive personal data, for example) never lands in your warehouse. When a new field appears, it becomes available for you to add. What happens to removed or renamed fields depends on a table's sync mode: full-refresh tables always match what's currently in Square, so dropped fields disappear, while incremental tables keep their existing columns and history, so an old field stays and newly added fields fill in over time.
How do I handle GDPR or data deletion requests?
Your data lives in your own Kaivo-managed BigQuery warehouse, so the most direct option is to delete or anonymise specific records right in BigQuery. If you delete data in Square instead, full-refresh tables drop it on the next sync, while incremental tables keep it, so you would remove the row in BigQuery or ask us to run a full refresh. To remove everything, delete the Square connector in Kaivo and all of its synced data is deleted with it.
Common use cases for Square data
Sales reporting
Combine payments and orders to track revenue and average sale over time and by location.
Product performance
Join orders with items and categories to see best sellers and margin.
Labour and shifts
Use shifts and team_member_wages to compare labour cost against sales.
Customer and loyalty
Bring customers and loyalty together to measure repeat visits and rewards.
Use Square data in your AI and BI tools
Once Square data lands in your Kaivo-managed BigQuery warehouse, you can explore it with AI tools or any BI tool that connects to BigQuery. Here's how the most common destinations work with Square data.
Claude
Use Kaivo's MCP server to give Claude secure, workspace-scoped access to your data. Setup guide →
Power BI
Microsoft's BI tool with a native BigQuery connector. Supports direct query and scheduled refresh. Setup guide →
Data Studio
Free Google BI tool with native BigQuery support. One-click connection to your Kaivo warehouse; great for SMB teams on Google Workspace. Setup guide →
Tableau
The premium analytics standard, with native BigQuery integration. Setup guide →
Google Sheets
Use Connected Sheets to query BigQuery directly from a spreadsheet, with no SQL. Setup guide →
Excel
Connect via Power Query's BigQuery connector. Setup guide →
Metabase
Open-source BI tool with strong BigQuery support. Setup guide →
See our pricing page for Square connector pricing and plan details.
Related connectors
- Chargebee: Sync Chargebee to BigQuery.
- ChartMogul: Sync ChartMogul to BigQuery.
- Fennoa: Sync Fennoa to BigQuery.
- Fortnox: Sync Fortnox to BigQuery.
- FreshBooks: Sync FreshBooks to BigQuery.
- PayPal Enterprise Payments: Sync PayPal Enterprise Payments to BigQuery.
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