DatriseAI-first ETL

Workday Raas Amazon Athena

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

How Datrise loads Workday Raas into Amazon Athena

Datrise syncs Workday Raas's records, events, and configuration objects 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

Workday Raas: SaaS or API data source for analytics and warehouse sync.

Amazon Athena: Serverless SQL over S3 data lake tables.

How Workday Raas entities map to Amazon Athena

Workday Raas entityAmazon Athena objectNotes
recordsworkday_raas_recordsid PK · custom fields → struct/map columns in Parquet
eventsworkday_raas_eventstimestamp events
configuration objectsworkday_raas_configuration_objectsid PK · linked to workday_raas_records

FAQ

How does Datrise handle Workday Raas'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 Workday Raas 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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