Square → Snowflake
AI-first ETL from Square into Snowflake. Governed entities, incremental sync, typed landing tables.
How Datrise loads Square into Snowflake
Datrise syncs Square's payments, orders, customers, catalog items, and locations into Snowflake as a typed table per source entity. Flexible or custom fields land in VARIANT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP_TZ.
Sync is incremental: Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size, so re-runs update only what changed. Automatic micro-partitioning, with optional clustering keys on high-cardinality ids. Snowflake upper-cases unquoted identifiers, so Datrise standardizes on lower-case quoted names to keep column references stable.
Ideal for central analytics warehouses feeding BI and AI workloads.
Endpoints
Square: Payments and commerce platform for retail and online sellers.
Snowflake: Cloud data warehouse with separated compute and storage.
How Square entities map to Snowflake
| Square entity | Snowflake object | Notes |
|---|---|---|
| payments | square_payments | id PK · custom fields → VARIANT columns |
| orders | square_orders | id PK · linked to square_payments |
| customers | square_customers | id PK · linked to square_payments |
| catalog items | square_catalog_items | id PK · linked to square_payments |
FAQ
How does Datrise handle Square's custom fields in Snowflake?
Flexible values are stored as VARIANT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Snowflake types.
How does the Square to Snowflake sync stay up to date?
It runs incrementally — Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size.
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