DatriseAI-first ETL

Harvest Forecast to Tableau Integration

AI-first ETL from Harvest Forecast into Tableau. Governed entities, incremental sync, typed landing tables.

How Datrise loads Harvest Forecast into Tableau

Datrise syncs Harvest Forecast's assignments, people, projects, clients, milestones, and scheduled hours 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 Forecast: Resource scheduling and project capacity planning.

Tableau: Salesforce analytics platform for interactive dashboards and visual exploration.

How Harvest Forecast entities map to Tableau

Harvest Forecast entityTableau objectNotes
assignmentsharvest_forecast_assignmentsid PK · custom fields → flattened columns for Tableau fields
peopleharvest_forecast_peopleid PK · linked to harvest_forecast_assignments
projectsharvest_forecast_projectsid PK · linked to harvest_forecast_assignments
clientsharvest_forecast_clientsid PK · linked to harvest_forecast_assignments

FAQ

How does Datrise handle Harvest Forecast'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 Forecast 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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