DatriseAI-first ETL

Harvest Forecast to MicroStrategy Integration

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

How Datrise loads Harvest Forecast into MicroStrategy

Datrise syncs Harvest Forecast's assignments, people, projects, clients, milestones, and scheduled hours into MicroStrategy as warehouse tables for MicroStrategy's schema objects. Flexible or custom fields land in flattened columns, and timestamps such as created, updated, and status changes are typed as date/time dimensions.

Sync is incremental: Datrise uses incremental refresh of the warehouse tables behind attributes and metrics, so re-runs update only what changed. Date-partitioned facts. MicroStrategy maps attributes to columns, so Datrise lands stable keys and names so metrics don't break.

Ideal for large-scale enterprise reporting and governance.

Endpoints

Harvest Forecast: Resource scheduling and project capacity planning.

MicroStrategy: Enterprise BI with dossiers, governed metrics, and mobility.

How Harvest Forecast entities map to MicroStrategy

Harvest Forecast entityMicroStrategy objectNotes
assignmentsharvest_forecast_assignmentsid PK · custom fields → flattened columns
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 MicroStrategy?

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

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

It runs incrementally — Datrise uses incremental refresh of the warehouse tables behind attributes and metrics.

Related pipelines

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