DatriseAI-first ETL

Workable to Databricks SQL Warehouse Integration

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

How Datrise loads Workable into Databricks SQL Warehouse

Datrise syncs Workable's candidates, jobs, applications, interviews, offers, and pipeline stages 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

Workable: Recruiting pipeline and candidate evaluation.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Workable entities map to Databricks SQL Warehouse

Workable entityDatabricks SQL Warehouse objectNotes
candidatesworkable_candidatesid PK · custom fields → VARIANT or STRUCT columns
jobsworkable_jobsid PK · linked to workable_candidates
applicationsworkable_applicationsid PK · linked to workable_candidates
interviewsworkable_interviewsid PK · linked to workable_candidates

FAQ

How does Datrise handle Workable'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 Workable 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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