DatriseAI-first ETL

Harvest Forecast to Apache Superset Integration

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

How Datrise loads Harvest Forecast into Apache Superset

Datrise syncs Harvest Forecast's assignments, people, projects, clients, milestones, and scheduled hours into Apache Superset as governed SQL tables Superset queries directly. Flexible or custom fields land in flattened columns for the explore UI, and timestamps such as created, updated, and status changes are typed as temporal columns for time-series charts.

Sync is incremental: Datrise uses incremental refresh of the queried tables, so re-runs update only what changed. Date-partitioned tables to keep dashboards responsive. Superset charts run live SQL, so Datrise lands query-friendly, indexed tables rather than wide raw payloads.

Ideal for open-source dashboards over your own database.

Endpoints

Harvest Forecast: Resource scheduling and project capacity planning.

Apache Superset: Open-source BI for SQL exploration, charts, and dashboard publishing.

How Harvest Forecast entities map to Apache Superset

Harvest Forecast entityApache Superset objectNotes
assignmentsharvest_forecast_assignmentsid PK · custom fields → flattened columns for the explore UI
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 Apache Superset?

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

How does the Harvest Forecast to Apache Superset sync stay up to date?

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

Related pipelines

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