DatriseAI-first ETL

Cart to Google BigQuery Integration

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

How Datrise loads Cart into Google BigQuery

Datrise syncs Cart's orders, products, customers, shipments, payments, and discounts 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

Cart: Ecommerce storefront and order management.

Google BigQuery: Serverless analytics warehouse on GCP.

How Cart entities map to Google BigQuery

Cart entityGoogle BigQuery objectNotes
orderscart_ordersid PK · custom fields → JSON or nested/repeated (STRUCT) columns
productscart_productsid PK · linked to cart_orders
customerscart_customersid PK · linked to cart_orders
shipmentscart_shipmentsid PK · linked to cart_orders

FAQ

How does Datrise handle Cart'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 Cart 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

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