DatriseAI-first ETL

Adobe Commerce (Magento) Looker

AI-first ETL from Adobe Commerce (Magento) into Looker. Governed entities, incremental sync, typed landing tables.

How Datrise loads Adobe Commerce (Magento) into Looker

Datrise syncs Adobe Commerce (Magento)'s orders, products, customers, carts, and store views 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

Adobe Commerce (Magento): Enterprise e-commerce catalog, orders, and customer data.

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

How Adobe Commerce (Magento) entities map to Looker

Adobe Commerce (Magento) entityLooker objectNotes
ordersmagento_ordersid PK · custom fields → flattened columns (nested fields expanded for modeling)
productsmagento_productsid PK · linked to magento_orders
customersmagento_customersid PK · linked to magento_orders
cartsmagento_cartsid PK · linked to magento_orders

FAQ

How does Datrise handle Adobe Commerce (Magento)'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 Adobe Commerce (Magento) 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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