DatriseAI-first ETL

Harvest Forecast to Looker Studio Integration

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

How Datrise loads Harvest Forecast into Looker Studio

Datrise syncs Harvest Forecast's assignments, people, projects, clients, milestones, and scheduled hours into Looker Studio as warehouse tables Looker Studio connects to. Flexible or custom fields land in flattened columns for chart fields, and timestamps such as created, updated, and status changes are typed as date dimension columns.

Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned tables to keep extract refresh fast. Looker Studio performs best on pre-aggregated tables, so Datrise lands tidy, report-shaped tables rather than raw API payloads.

Ideal for free, shareable dashboards on Google data sources.

Endpoints

Harvest Forecast: Resource scheduling and project capacity planning.

Looker Studio: Google self-service dashboards and reporting (formerly Data Studio).

How Harvest Forecast entities map to Looker Studio

Harvest Forecast entityLooker Studio objectNotes
assignmentsharvest_forecast_assignmentsid PK · custom fields → flattened columns for chart 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 Looker Studio?

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

How does the Harvest Forecast to Looker Studio sync stay up to date?

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

Related pipelines

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