SAP → Amazon Athena
AI-first ETL from SAP into Amazon Athena. Governed entities, incremental sync, typed landing tables.
How Datrise loads SAP into Amazon Athena
Datrise syncs SAP's finance, procurement, operations, and master-data entities 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: ERP source for finance, operations, and procurement entities.
Amazon Athena: Serverless SQL over S3 data lake tables.
How SAP entities map to Amazon Athena
| SAP entity | Amazon Athena object | Notes |
|---|---|---|
| finance | sap_finance | id PK · custom fields → struct/map columns in Parquet |
| procurement | sap_procurement | id PK · linked to sap_finance |
| operations | sap_operations | id PK · linked to sap_finance |
| master-data entities | sap_master_data_entities | id PK · linked to sap_finance |
FAQ
How does Datrise handle SAP'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 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.
Related pipelines
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