DatriseAI-first ETL

Harvest Forecast to Sisense Integration

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

How Datrise loads Harvest Forecast into Sisense

Datrise syncs Harvest Forecast's assignments, people, projects, clients, milestones, and scheduled hours into Sisense as modeled tables for a Sisense ElastiCube (or live connection). Flexible or custom fields land in flattened columns for the cube, and timestamps such as created, updated, and status changes are typed as date/time fields.

Sync is incremental: Datrise uses incremental ElastiCube builds on changed rows, so re-runs update only what changed. Date-partitioned facts to speed cube builds. ElastiCube is an in-memory model, so Datrise lands incremental, build-friendly tables rather than forcing full rebuilds.

Ideal for embedded analytics on an in-memory engine.

Endpoints

Harvest Forecast: Resource scheduling and project capacity planning.

Sisense: Analytics platform with elastic data models and embedded analytics.

How Harvest Forecast entities map to Sisense

Harvest Forecast entitySisense objectNotes
assignmentsharvest_forecast_assignmentsid PK · custom fields → flattened columns for the cube
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 Sisense?

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

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

It runs incrementally — Datrise uses incremental ElastiCube builds on changed rows.

Related pipelines

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