DatriseAI-first ETL

Greenhouse to GoodData Integration

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

How Datrise loads Greenhouse into GoodData

Datrise syncs Greenhouse's candidates, applications, jobs, interviews, scorecards, and offers into GoodData as warehouse tables GoodData maps into its logical data model. Flexible or custom fields land in flattened columns, and timestamps such as created, updated, and status changes are typed as date dimensions.

Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned facts. GoodData's LDM maps datasets by keys, so Datrise lands stable primary and foreign id columns to keep the model valid.

Ideal for embedded, multi-tenant analytics.

Endpoints

Greenhouse: Applicant tracking and structured hiring.

GoodData: Composable analytics platform with headless BI and embedded dashboards.

How Greenhouse entities map to GoodData

Greenhouse entityGoodData objectNotes
candidatesgreenhouse_candidatesid PK · custom fields → flattened columns
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 GoodData?

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 GoodData types.

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

It runs incrementally — Datrise uses incremental refresh of the connected tables.

Related pipelines

Early access

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