DatriseAI-first ETL

Shiphero to Amazon Redshift Integration

AI-first ETL from Shiphero into Amazon Redshift. Governed entities, incremental sync, typed landing tables.

How Datrise loads Shiphero into Amazon Redshift

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

Shiphero: Warehouse management and fulfillment operations.

Amazon Redshift: AWS petabyte-scale warehouse with Spectrum.

How Shiphero entities map to Amazon Redshift

Shiphero entityAmazon Redshift objectNotes
ordersshiphero_ordersid PK · custom fields → SUPER 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 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 Shiphero 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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