DatriseAI-first ETL

Shopify Databricks SQL Warehouse

AI-first ETL from Shopify into Databricks SQL Warehouse. Governed entities, incremental sync, typed landing tables.

How Datrise loads Shopify into Databricks SQL Warehouse

Datrise syncs Shopify's orders, products, customers, inventory levels, and fulfillment events into Databricks SQL Warehouse as a Delta Lake table per source entity. Flexible or custom fields land in VARIANT or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses a Delta MERGE on stable id, with change history available via time travel, so re-runs update only what changed. Delta partitioning by load date with OPTIMIZE/Z-ORDER on query keys. Datrise writes Unity Catalog–governed Delta tables, so lineage and permissions are managed centrally rather than per-notebook.

Ideal for lakehouse analytics and ML feature tables on Databricks.

Endpoints

Shopify: E-commerce platform for orders, catalog, and customer data.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Shopify entities map to Databricks SQL Warehouse

Shopify entityDatabricks SQL Warehouse objectNotes
ordersshopify_ordersid PK · custom fields → VARIANT or STRUCT columns
productsshopify_productsid PK · linked to shopify_orders
customersshopify_customersid PK · linked to shopify_orders
inventory levelsshopify_inventory_levelsid PK · linked to shopify_orders

FAQ

How does Datrise handle Shopify's custom fields in Databricks SQL Warehouse?

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

How does the Shopify to Databricks SQL Warehouse sync stay up to date?

It runs incrementally — Datrise uses a Delta MERGE on stable id, with change history available via time travel.

Related pipelines

Early access

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