Harvest → Tableau
AI-first ETL from Harvest into Tableau. Governed entities, incremental sync, typed landing tables.
How Datrise loads Harvest into Tableau
Datrise syncs Harvest's time entries, projects, clients, invoices, and utilization into Tableau as warehouse tables or a refreshed .hyper extract. Flexible or custom fields land in flattened columns for Tableau fields, and timestamps such as created, updated, and status changes are typed as date/datetime fields.
Sync is incremental: Datrise uses incremental refresh of the tables behind a live connection or extract, so re-runs update only what changed. Date-partitioned facts to keep extract refresh quick. Tableau .hyper extracts snapshot data, so Datrise keeps the source tables incremental and lets you choose live vs extract.
Ideal for visual analytics and dashboards in Tableau.
Endpoints
Harvest: Time tracking and project profitability for services teams.
Tableau: Salesforce analytics platform for interactive dashboards and visual exploration.
How Harvest entities map to Tableau
| Harvest entity | Tableau object | Notes |
|---|---|---|
| time entries | harvest_time_entries | id PK · custom fields → flattened columns for Tableau fields |
| 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 Tableau?
Flexible values are stored as flattened columns for Tableau fields, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Tableau types.
How does the Harvest to Tableau sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the tables behind a live connection or extract.
Related pipelines
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