DatriseAI-first ETL

SAP Fieldglass to Snowflake Integration

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

How Datrise loads SAP Fieldglass into Snowflake

Datrise syncs SAP Fieldglass's workers, job postings, work orders, time sheets, suppliers, and invoices into Snowflake as a typed table per source entity. Flexible or custom fields land in VARIANT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP_TZ.

Sync is incremental: Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size, so re-runs update only what changed. Automatic micro-partitioning, with optional clustering keys on high-cardinality ids. Snowflake upper-cases unquoted identifiers, so Datrise standardizes on lower-case quoted names to keep column references stable.

Ideal for central analytics warehouses feeding BI and AI workloads.

Endpoints

SAP Fieldglass: External workforce and contingent labor management.

Snowflake: Cloud data warehouse with separated compute and storage.

How SAP Fieldglass entities map to Snowflake

SAP Fieldglass entitySnowflake objectNotes
workerssap_fieldglass_workersid PK · custom fields → VARIANT columns
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 Snowflake?

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

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

It runs incrementally — Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size.

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