Greenhouse to Sisense Integration
AI-first ETL from Greenhouse into Sisense. Governed entities, incremental sync, typed landing tables.
How Datrise loads Greenhouse into Sisense
Datrise syncs Greenhouse's candidates, applications, jobs, interviews, scorecards, and offers 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
Greenhouse: Applicant tracking and structured hiring.
Sisense: Analytics platform with elastic data models and embedded analytics.
How Greenhouse entities map to Sisense
| Greenhouse entity | Sisense object | Notes |
|---|---|---|
| candidates | greenhouse_candidates | id PK · custom fields → flattened columns for the cube |
| applications | greenhouse_applications | id PK · linked to greenhouse_candidates |
| jobs | greenhouse_jobs | id PK · linked to greenhouse_candidates |
| interviews | greenhouse_interviews | id PK · linked to greenhouse_candidates |
FAQ
How does Datrise handle Greenhouse'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 Greenhouse to Sisense sync stay up to date?
It runs incrementally — Datrise uses incremental ElastiCube builds on changed rows.
Related pipelines
More destinations for Greenhouse
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