DatriseAI-first ETL

Adobe Commerce (Magento) Snowflake

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

How Datrise loads Adobe Commerce (Magento) into Snowflake

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

Sync is incremental: Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size, so re-runs update only what changed. Automatic micro-partitioning, with optional clustering keys on high-cardinality ids. Snowflake upper-cases unquoted identifiers, so Datrise standardizes on lower-case quoted names to keep column references stable.

Ideal for central analytics warehouses feeding BI and AI workloads.

Endpoints

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

Snowflake: Cloud data warehouse with separated compute and storage.

How Adobe Commerce (Magento) entities map to Snowflake

Adobe Commerce (Magento) entitySnowflake objectNotes
ordersmagento_ordersid PK · custom fields → VARIANT 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 Snowflake?

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

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

It runs incrementally — Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size.

Related pipelines

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