Harvest → Amazon S3 Data Lake
AI-first ETL from Harvest into Amazon S3 Data Lake. Governed entities, incremental sync, typed landing tables.
How Datrise loads Harvest into Amazon S3 Data Lake
Datrise syncs Harvest's time entries, projects, clients, invoices, and utilization 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
Harvest: Time tracking and project profitability for services teams.
Amazon S3 Data Lake: Object storage landing zone for parquet and snapshots.
How Harvest entities map to Amazon S3 Data Lake
| Harvest entity | Amazon S3 Data Lake object | Notes |
|---|---|---|
| time entries | harvest_time_entries | id PK · custom fields → nested struct/map fields in Parquet |
| projects | harvest_projects | id PK · linked to harvest_time_entries |
| clients | harvest_clients | id PK · linked to harvest_time_entries |
| invoices | harvest_invoices | id PK · linked to harvest_time_entries |
FAQ
How does Datrise handle Harvest'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 Harvest 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.
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Early access
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