DatriseAI-first ETL

Primetric to Sisense Integration

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

How Datrise loads Primetric into Sisense

Datrise syncs Primetric's projects, assignments, people, timesheets, budgets, and utilization rates 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

Primetric: Project profitability and resource forecasting.

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

How Primetric entities map to Sisense

Primetric entitySisense objectNotes
projectsprimetric_projectsid PK · custom fields → flattened columns for the cube
assignmentsprimetric_assignmentsid PK · linked to primetric_projects
peopleprimetric_peopleid PK · linked to primetric_projects
timesheetsprimetric_timesheetsid PK · linked to primetric_projects

FAQ

How does Datrise handle Primetric'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 Primetric to Sisense sync stay up to date?

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

Related pipelines

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