DatriseAI-first ETL

Harvest Forecast to Amazon QuickSight Integration

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

How Datrise loads Harvest Forecast into Amazon QuickSight

Datrise syncs Harvest Forecast's assignments, people, projects, clients, milestones, and scheduled hours into Amazon QuickSight as warehouse tables or a SPICE-loaded dataset. Flexible or custom fields land in flattened columns for analyses, and timestamps such as created, updated, and status changes are typed as date/time fields.

Sync is incremental: Datrise uses incremental refresh of the tables behind SPICE or direct query, so re-runs update only what changed. Date-partitioned facts to bound SPICE refresh. QuickSight SPICE is an in-memory copy, so Datrise keeps the backing tables incremental so refreshes stay cheap.

Ideal for AWS-native dashboards with pay-per-session pricing.

Endpoints

Harvest Forecast: Resource scheduling and project capacity planning.

Amazon QuickSight: AWS serverless BI with SPICE and embedded analytics.

How Harvest Forecast entities map to Amazon QuickSight

Harvest Forecast entityAmazon QuickSight objectNotes
assignmentsharvest_forecast_assignmentsid PK · custom fields → flattened columns for analyses
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 Amazon QuickSight?

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

How does the Harvest Forecast to Amazon QuickSight sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the tables behind SPICE or direct query.

Related pipelines

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