SAP Fieldglass to Google BigQuery Integration
AI-first ETL from SAP Fieldglass into Google BigQuery. Governed entities, incremental sync, typed landing tables.
How Datrise loads SAP Fieldglass into Google BigQuery
Datrise syncs SAP Fieldglass's workers, job postings, work orders, time sheets, suppliers, and invoices into Google BigQuery as a partitioned table per source entity. Flexible or custom fields land in JSON or nested/repeated (STRUCT) columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.
Sync is incremental: Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target, so re-runs update only what changed. Partition by ingestion or event date and cluster by entity id to keep scanned bytes low. BigQuery bills by bytes scanned, so Datrise partitions and clusters every table to keep query costs predictable.
Ideal for Google-stack analytics and ML on serverless infrastructure.
Endpoints
SAP Fieldglass: External workforce and contingent labor management.
Google BigQuery: Serverless analytics warehouse on GCP.
How SAP Fieldglass entities map to Google BigQuery
| SAP Fieldglass entity | Google BigQuery object | Notes |
|---|---|---|
| workers | sap_fieldglass_workers | id PK · custom fields → JSON or nested/repeated (STRUCT) columns |
| job postings | sap_fieldglass_job_postings | id PK · linked to sap_fieldglass_workers |
| work orders | sap_fieldglass_work_orders | id PK · linked to sap_fieldglass_workers |
| time sheets | sap_fieldglass_time_sheets | id PK · linked to sap_fieldglass_workers |
FAQ
How does Datrise handle SAP Fieldglass's custom fields in Google BigQuery?
Flexible values are stored as JSON or nested/repeated (STRUCT) columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Google BigQuery types.
How does the SAP Fieldglass to Google BigQuery sync stay up to date?
It runs incrementally — Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target.
Related pipelines
More destinations for SAP Fieldglass
- SAP Fieldglass → Amazon Redshift
- SAP Fieldglass → Databricks SQL Warehouse
- SAP Fieldglass → ClickHouse
- SAP Fieldglass → DuckDB
- SAP Fieldglass → Amazon Athena
- SAP Fieldglass → Amazon S3 Data Lake
- SAP Fieldglass → Azure Data Lake Storage
- SAP Fieldglass → Azure Synapse
- SAP Fieldglass → Spreadsheets
- SAP Fieldglass → Airtable
- SAP Fieldglass → CSV Files
- SAP Fieldglass → MongoDB
More sources for Google BigQuery
- Secoda → Google BigQuery
- Selligent → Google BigQuery
- SendGrid → Google BigQuery
- Sendwithus → Google BigQuery
- Senseforce → Google BigQuery
- Sentry → Google BigQuery
- SFTP Bulk → Google BigQuery
- Shiphero → Google BigQuery
- Shippo → Google BigQuery
- Short.io → Google BigQuery
- Slack → Google BigQuery
- Smaily → Google BigQuery
Early access
Connect SAP Fieldglass to Google BigQuery the easy way
Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.