DatriseAI-first ETL

Harvest Forecast to Chartio Integration

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

How Datrise loads Harvest Forecast into Chartio

Datrise syncs Harvest Forecast's assignments, people, projects, clients, milestones, and scheduled hours into Chartio as SQL tables a visual-SQL explorer connects to. Flexible or custom fields land in flattened columns for visual SQL, and timestamps such as created, updated, and status changes are typed as temporal columns.

Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned facts. Visual-SQL tools build joins from your schema, so Datrise lands clearly related tables with stable id columns.

Ideal for drag-and-drop charting over a database.

Endpoints

Harvest Forecast: Resource scheduling and project capacity planning.

Chartio: Cloud BI for exploring warehouse data with drag-and-drop charts.

How Harvest Forecast entities map to Chartio

Harvest Forecast entityChartio objectNotes
assignmentsharvest_forecast_assignmentsid PK · custom fields → flattened columns for visual SQL
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 Chartio?

Flexible values are stored as flattened columns for visual SQL, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Chartio types.

How does the Harvest Forecast to Chartio sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the connected tables.

Related pipelines

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