DatriseAI-first ETL

Adobe Commerce (Magento) Amazon Redshift

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

How Datrise loads Adobe Commerce (Magento) into Amazon Redshift

Datrise syncs Adobe Commerce (Magento)'s orders, products, customers, carts, and store views into Amazon Redshift as a typed table per source entity. Flexible or custom fields land in SUPER columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMPTZ.

Sync is incremental: Datrise uses COPY from staged files, then a delete-and-insert merge on stable id, so re-runs update only what changed. A DISTKEY on the join id and a SORTKEY on the load timestamp. Redshift performance hinges on dist/sort keys, so Datrise picks them from your entity ids and sync timestamps rather than defaulting to EVEN distribution.

Ideal for AWS-native warehouses already using the Redshift ecosystem.

Endpoints

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

Amazon Redshift: AWS petabyte-scale warehouse with Spectrum.

How Adobe Commerce (Magento) entities map to Amazon Redshift

Adobe Commerce (Magento) entityAmazon Redshift objectNotes
ordersmagento_ordersid PK · custom fields → SUPER columns
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 Amazon Redshift?

Flexible values are stored as SUPER columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon Redshift types.

How does the Adobe Commerce (Magento) to Amazon Redshift sync stay up to date?

It runs incrementally — Datrise uses COPY from staged files, then a delete-and-insert merge on stable id.

Related pipelines

Early access

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