DatriseAI-first ETL

Sap Fieldglass Amazon Redshift

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

How Datrise loads Sap Fieldglass into Amazon Redshift

Datrise syncs Sap Fieldglass's records, events, and configuration objects 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 Fieldglass: SaaS or API data source for analytics and warehouse sync.

Amazon Redshift: AWS petabyte-scale warehouse with Spectrum.

How Sap Fieldglass entities map to Amazon Redshift

Sap Fieldglass entityAmazon Redshift objectNotes
recordssap_fieldglass_recordsid PK · custom fields → SUPER columns
eventssap_fieldglass_eventsTIMESTAMPTZ events
configuration objectssap_fieldglass_configuration_objectsid PK · linked to sap_fieldglass_records

FAQ

How does Datrise handle Sap Fieldglass'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 Fieldglass 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

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