DatriseAI-first ETL

Shiphero to Databricks SQL Warehouse Integration

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

How Datrise loads Shiphero into Databricks SQL Warehouse

Datrise syncs Shiphero's orders, shipments, products, inventory levels, warehouses, and returns 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

Shiphero: Warehouse management and fulfillment operations.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Shiphero entities map to Databricks SQL Warehouse

Shiphero entityDatabricks SQL Warehouse objectNotes
ordersshiphero_ordersid PK · custom fields → VARIANT or STRUCT columns
shipmentsshiphero_shipmentsid PK · linked to shiphero_orders
productsshiphero_productsid PK · linked to shiphero_orders
inventory levelsshiphero_inventory_levelsid PK · linked to shiphero_orders

FAQ

How does Datrise handle Shiphero'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 Shiphero 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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