DatriseAI-first ETL

Harvest MicroStrategy

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

How Datrise loads Harvest into MicroStrategy

Datrise syncs Harvest's time entries, projects, clients, invoices, and utilization 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: Time tracking and project profitability for services teams.

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

How Harvest entities map to MicroStrategy

Harvest entityMicroStrategy objectNotes
time entriesharvest_time_entriesid PK · custom fields → flattened columns
projectsharvest_projectsid PK · linked to harvest_time_entries
clientsharvest_clientsid PK · linked to harvest_time_entries
invoicesharvest_invoicesid PK · linked to harvest_time_entries

FAQ

How does Datrise handle Harvest'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 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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