DatriseAI-first ETL

SAP Fieldglass to Amazon Athena Integration

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

How Datrise loads SAP Fieldglass into Amazon Athena

Datrise syncs SAP Fieldglass's workers, job postings, work orders, time sheets, suppliers, and invoices into Amazon Athena as partitioned Parquet in S3 exposed as an Athena table. Flexible or custom fields land in struct/map columns in Parquet, and timestamps such as created, updated, and status changes are typed as timestamp.

Sync is incremental: Datrise uses writes new Parquet partitions and registers them in the Glue Data Catalog, so re-runs update only what changed. Hive-style partitioning by load date so Athena scans only new data. Athena bills per byte scanned and small files hurt, so Datrise compacts to right-sized Parquet rather than many tiny objects.

Ideal for serverless SQL over an S3 lake without a running warehouse.

Endpoints

SAP Fieldglass: External workforce and contingent labor management.

Amazon Athena: Serverless SQL over S3 data lake tables.

How SAP Fieldglass entities map to Amazon Athena

SAP Fieldglass entityAmazon Athena objectNotes
workerssap_fieldglass_workersid PK · custom fields → struct/map columns in Parquet
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 Amazon Athena?

Flexible values are stored as struct/map columns in Parquet, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon Athena types.

How does the SAP Fieldglass to Amazon Athena sync stay up to date?

It runs incrementally — Datrise uses writes new Parquet partitions and registers them in the Glue Data Catalog.

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