DatriseAI-first ETL

Square Google BigQuery

AI-first ETL from Square into Google BigQuery. Governed entities, incremental sync, typed landing tables.

How Datrise loads Square into Google BigQuery

Datrise syncs Square's payments, orders, customers, catalog items, and locations into Google BigQuery as a partitioned table per source entity. Flexible or custom fields land in JSON or nested/repeated (STRUCT) columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target, so re-runs update only what changed. Partition by ingestion or event date and cluster by entity id to keep scanned bytes low. BigQuery bills by bytes scanned, so Datrise partitions and clusters every table to keep query costs predictable.

Ideal for Google-stack analytics and ML on serverless infrastructure.

Endpoints

Square: Payments and commerce platform for retail and online sellers.

Google BigQuery: Serverless analytics warehouse on GCP.

How Square entities map to Google BigQuery

Square entityGoogle BigQuery objectNotes
paymentssquare_paymentsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
orderssquare_ordersid PK · linked to square_payments
customerssquare_customersid PK · linked to square_payments
catalog itemssquare_catalog_itemsid PK · linked to square_payments

FAQ

How does Datrise handle Square's custom fields in Google BigQuery?

Flexible values are stored as JSON or nested/repeated (STRUCT) columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Google BigQuery types.

How does the Square to Google BigQuery sync stay up to date?

It runs incrementally — Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target.

Related pipelines

Early access

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