DatriseAI-first ETL

Salesforce Commerce Cloud to Looker Integration

AI-first ETL from Salesforce Commerce Cloud into Looker. Governed entities, incremental sync, typed landing tables.

How Datrise loads Salesforce Commerce Cloud into Looker

Datrise syncs Salesforce Commerce Cloud's orders, products, customers, baskets, promotions, and inventory records into Looker as governed warehouse tables with LookML-ready naming. Flexible or custom fields land in flattened columns (nested fields expanded for modeling), and timestamps such as created, updated, and status changes are typed as date/time dimension columns.

Sync is incremental: Datrise uses incremental refresh of the underlying warehouse tables Looker explores, so re-runs update only what changed. Date-partitioned fact tables for PDT performance. Looker models live in LookML on top of SQL, so Datrise lands clean, stable column names rather than churn that would break your views.

Ideal for governed, version-controlled BI on a warehouse.

Endpoints

Salesforce Commerce Cloud: Salesforce storefront, catalog, and order management.

Looker: Google Cloud BI with LookML semantic models and governed dashboards.

How Salesforce Commerce Cloud entities map to Looker

Salesforce Commerce Cloud entityLooker objectNotes
orderssalesforce_commerce_cloud_ordersid PK · custom fields → flattened columns (nested fields expanded for modeling)
productssalesforce_commerce_cloud_productsid PK · linked to salesforce_commerce_cloud_orders
customerssalesforce_commerce_cloud_customersid PK · linked to salesforce_commerce_cloud_orders
basketssalesforce_commerce_cloud_basketsid PK · linked to salesforce_commerce_cloud_orders

FAQ

How does Datrise handle Salesforce Commerce Cloud's custom fields in Looker?

Flexible values are stored as flattened columns (nested fields expanded for modeling), so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Looker types.

How does the Salesforce Commerce Cloud to Looker sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the underlying warehouse tables Looker explores.

Related pipelines

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