DatriseAI-first ETL

Workday Amazon S3 Data Lake

AI-first ETL from Workday into Amazon S3 Data Lake. Governed entities, incremental sync, typed landing tables.

How Datrise loads Workday into Amazon S3 Data Lake

Datrise syncs Workday's HR, finance entities, organizational structures, and operational events into Amazon S3 Data Lake as columnar Parquet objects partitioned per source entity. Flexible or custom fields land in nested struct/map fields in Parquet, and timestamps such as created, updated, and status changes are typed as ISO-8601 timestamp columns.

Sync is incremental: Datrise uses writes new date partitions and compacts small files on a schedule, so re-runs update only what changed. Hive-style path partitioning (entity/date) for engine-agnostic reads. A lake has no schema enforcement, so Datrise writes a schema manifest alongside the data to keep downstream engines consistent.

Ideal for an open, engine-neutral storage layer for Spark, Athena, Trino, or DuckDB.

Endpoints

Workday: Enterprise HR and finance source for operational reporting.

Amazon S3 Data Lake: Object storage landing zone for parquet and snapshots.

How Workday entities map to Amazon S3 Data Lake

Workday entityAmazon S3 Data Lake objectNotes
HRworkday_hrid PK · custom fields → nested struct/map fields in Parquet
finance entitiesworkday_finance_entitiesid PK · linked to workday_hr
organizational structuresworkday_organizational_structuresid PK · linked to workday_hr
operational eventsworkday_operational_eventsISO-8601 timestamp columns events

FAQ

How does Datrise handle Workday's custom fields in Amazon S3 Data Lake?

Flexible values are stored as nested struct/map fields in Parquet, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon S3 Data Lake types.

How does the Workday to Amazon S3 Data Lake sync stay up to date?

It runs incrementally — Datrise uses writes new date partitions and compacts small files on a schedule.

Related pipelines

Early access

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