SAP → Amazon Redshift
AI-first ETL from SAP into Amazon Redshift. Governed entities, incremental sync, typed landing tables.
How Datrise loads SAP into Amazon Redshift
Datrise syncs SAP's finance, procurement, operations, and master-data entities 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
SAP: ERP source for finance, operations, and procurement entities.
Amazon Redshift: AWS petabyte-scale warehouse with Spectrum.
How SAP entities map to Amazon Redshift
| SAP entity | Amazon Redshift object | Notes |
|---|---|---|
| finance | sap_finance | id PK · custom fields → SUPER columns |
| procurement | sap_procurement | id PK · linked to sap_finance |
| operations | sap_operations | id PK · linked to sap_finance |
| master-data entities | sap_master_data_entities | id PK · linked to sap_finance |
FAQ
How does Datrise handle SAP'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 SAP 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.
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Early access
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