DatriseAI-first ETL

Harvest Forecast to Spotfire Integration

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

How Datrise loads Harvest Forecast into Spotfire

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

Spotfire: Visual analytics platform for interactive dashboards and data science workflows.

How Harvest Forecast entities map to Spotfire

Harvest Forecast entitySpotfire objectNotes
assignmentsharvest_forecast_assignmentsid PK · custom fields → flattened columns for visualizations
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 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 Forecast to Spotfire sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the connected tables or in-memory data.

Related pipelines

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