DatriseAI-first ETL

SAP Fieldglass to Spotfire Integration

AI-first ETL from SAP Fieldglass into Spotfire. Governed entities, incremental sync, typed landing tables.

How Datrise loads SAP Fieldglass into Spotfire

Datrise syncs SAP Fieldglass's workers, job postings, work orders, time sheets, suppliers, and invoices into Spotfire as warehouse tables or in-memory data for Spotfire analyses. Flexible or custom fields land in flattened columns for visualizations, and timestamps such as created, updated, and status changes are typed as date/time columns.

Sync is incremental: Datrise uses incremental refresh of the connected tables or in-memory data, so re-runs update only what changed. Date-partitioned facts. Spotfire can load data in-memory, so Datrise keeps the backing tables incremental so analyses refresh without full reloads.

Ideal for interactive analytical visualization and data science.

Endpoints

SAP Fieldglass: External workforce and contingent labor management.

Spotfire: Visual analytics platform for interactive dashboards and data science workflows.

How SAP Fieldglass entities map to Spotfire

SAP Fieldglass entitySpotfire objectNotes
workerssap_fieldglass_workersid PK · custom fields → flattened columns for visualizations
job postingssap_fieldglass_job_postingsid PK · linked to sap_fieldglass_workers
work orderssap_fieldglass_work_ordersid PK · linked to sap_fieldglass_workers
time sheetssap_fieldglass_time_sheetsid PK · linked to sap_fieldglass_workers

FAQ

How does Datrise handle SAP Fieldglass's custom fields in Spotfire?

Flexible values are stored as flattened columns for visualizations, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Spotfire types.

How does the SAP Fieldglass to Spotfire sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the connected tables or in-memory data.

Related pipelines

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