Harvest → Snowflake
AI-first ETL from Harvest into Snowflake. Governed entities, incremental sync, typed landing tables.
How Datrise loads Harvest into Snowflake
Datrise syncs Harvest's time entries, projects, clients, invoices, and utilization into Snowflake as a typed table per source entity. Flexible or custom fields land in VARIANT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP_TZ.
Sync is incremental: Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size, so re-runs update only what changed. Automatic micro-partitioning, with optional clustering keys on high-cardinality ids. Snowflake upper-cases unquoted identifiers, so Datrise standardizes on lower-case quoted names to keep column references stable.
Ideal for central analytics warehouses feeding BI and AI workloads.
Endpoints
Harvest: Time tracking and project profitability for services teams.
Snowflake: Cloud data warehouse with separated compute and storage.
How Harvest entities map to Snowflake
| Harvest entity | Snowflake object | Notes |
|---|---|---|
| time entries | harvest_time_entries | id PK · custom fields → VARIANT columns |
| 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 Snowflake?
Flexible values are stored as VARIANT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Snowflake types.
How does the Harvest to Snowflake sync stay up to date?
It runs incrementally — Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size.
Related pipelines
More destinations for Harvest
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