DatriseAI-first ETL

Recruitee to Databricks SQL Warehouse Integration

AI-first ETL from Recruitee into Databricks SQL Warehouse. Governed entities, incremental sync, typed landing tables.

How Datrise loads Recruitee into Databricks SQL Warehouse

Datrise syncs Recruitee's candidates, offers, jobs, interviews, pipeline stages, and evaluations into Databricks SQL Warehouse as a Delta Lake table per source entity. Flexible or custom fields land in VARIANT or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses a Delta MERGE on stable id, with change history available via time travel, so re-runs update only what changed. Delta partitioning by load date with OPTIMIZE/Z-ORDER on query keys. Datrise writes Unity Catalog–governed Delta tables, so lineage and permissions are managed centrally rather than per-notebook.

Ideal for lakehouse analytics and ML feature tables on Databricks.

Endpoints

Recruitee: Collaborative hiring and candidate pipelines.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Recruitee entities map to Databricks SQL Warehouse

Recruitee entityDatabricks SQL Warehouse objectNotes
candidatesrecruitee_candidatesid PK · custom fields → VARIANT or STRUCT columns
offersrecruitee_offersid PK · linked to recruitee_candidates
jobsrecruitee_jobsid PK · linked to recruitee_candidates
interviewsrecruitee_interviewsid PK · linked to recruitee_candidates

FAQ

How does Datrise handle Recruitee's custom fields in Databricks SQL Warehouse?

Flexible values are stored as VARIANT or STRUCT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Databricks SQL Warehouse types.

How does the Recruitee to Databricks SQL Warehouse sync stay up to date?

It runs incrementally — Datrise uses a Delta MERGE on stable id, with change history available via time travel.

Related pipelines

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