DatriseAI-first ETL

Greenhouse to Spotfire Integration

AI-first ETL from Greenhouse into Spotfire. Governed entities, incremental sync, typed landing tables.

How Datrise loads Greenhouse into Spotfire

Datrise syncs Greenhouse's candidates, applications, jobs, interviews, scorecards, and offers into Spotfire as warehouse tables or in-memory data for Spotfire analyses. Flexible or custom fields land in flattened columns for visualizations, and timestamps such as created, updated, and status changes are typed as date/time columns.

Sync is incremental: Datrise uses incremental refresh of the connected tables or in-memory data, so re-runs update only what changed. Date-partitioned facts. Spotfire can load data in-memory, so Datrise keeps the backing tables incremental so analyses refresh without full reloads.

Ideal for interactive analytical visualization and data science.

Endpoints

Greenhouse: Applicant tracking and structured hiring.

Spotfire: Visual analytics platform for interactive dashboards and data science workflows.

How Greenhouse entities map to Spotfire

Greenhouse entitySpotfire objectNotes
candidatesgreenhouse_candidatesid PK · custom fields → flattened columns for visualizations
applicationsgreenhouse_applicationsid PK · linked to greenhouse_candidates
jobsgreenhouse_jobsid PK · linked to greenhouse_candidates
interviewsgreenhouse_interviewsid PK · linked to greenhouse_candidates

FAQ

How does Datrise handle Greenhouse's custom fields in Spotfire?

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

How does the Greenhouse to Spotfire sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the connected tables or in-memory data.

Related pipelines

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