Harvest → Mode
AI-first ETL from Harvest into Mode. Governed entities, incremental sync, typed landing tables.
How Datrise loads Harvest into Mode
Datrise syncs Harvest's time entries, projects, clients, invoices, and utilization into Mode as warehouse tables Mode queries with SQL. Flexible or custom fields land in flattened columns for SQL and notebooks, and timestamps such as created, updated, and status changes are typed as temporal columns.
Sync is incremental: Datrise uses incremental refresh of the queried tables, so re-runs update only what changed. Date-partitioned facts for report queries. Mode runs analyst-written SQL, so Datrise lands stable, documented tables that won't break saved reports.
Ideal for SQL-first analysis with Python and R notebooks.
Endpoints
Harvest: Time tracking and project profitability for services teams.
Mode: Collaborative analytics workspace for SQL, Python, and shared reports.
How Harvest entities map to Mode
| Harvest entity | Mode object | Notes |
|---|---|---|
| time entries | harvest_time_entries | id PK · custom fields → flattened columns for SQL and notebooks |
| 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 Mode?
Flexible values are stored as flattened columns for SQL and notebooks, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Mode types.
How does the Harvest to Mode sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the queried tables.
Related pipelines
More destinations for Harvest
Early access
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