Harvest → Spotfire
AI-first ETL from Harvest into Spotfire. Governed entities, incremental sync, typed landing tables.
How Datrise loads Harvest into Spotfire
Datrise syncs Harvest's time entries, projects, clients, invoices, and utilization into Spotfire as warehouse tables or in-memory data for Spotfire analyses. Flexible or custom fields land in flattened columns for visualizations, and timestamps such as created, updated, and status changes are typed as date/time columns.
Sync is incremental: Datrise uses incremental refresh of the connected tables or in-memory data, so re-runs update only what changed. Date-partitioned facts. Spotfire can load data in-memory, so Datrise keeps the backing tables incremental so analyses refresh without full reloads.
Ideal for interactive analytical visualization and data science.
Endpoints
Harvest: Time tracking and project profitability for services teams.
Spotfire: Visual analytics platform for interactive dashboards and data science workflows.
How Harvest entities map to Spotfire
| Harvest entity | Spotfire object | Notes |
|---|---|---|
| time entries | harvest_time_entries | id PK · custom fields → flattened columns for visualizations |
| 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 Spotfire?
Flexible values are stored as flattened columns for visualizations, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Spotfire types.
How does the Harvest to Spotfire sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables or in-memory data.
Related pipelines
More destinations for Harvest
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