Workable to DuckDB Integration

How Datrise loads Workable into DuckDB

Datrise syncs Workable's candidates, jobs, applications, interviews, offers, and pipeline stages into DuckDB as a typed table per source entity in a DuckDB file. Flexible or custom fields land in JSON or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP WITH TIME ZONE.

Sync is incremental: Datrise uses rewrites changed entities into the local database (or Parquet) on each run, so re-runs update only what changed. Hive-partitioned Parquet by load date when exporting. DuckDB is single-writer and embedded, so Datrise produces a consistent file snapshot rather than concurrent streaming writes.

Ideal for local and notebook analytics without standing up a server.

Endpoints

Workable: Recruiting pipeline and candidate evaluation.

DuckDB: In-process analytics database for fast local OLAP.

How Workable entities map to DuckDB

Workable entity	DuckDB object	Notes
candidates	`workable_candidates`	id PK · custom fields → JSON or STRUCT columns
jobs	`workable_jobs`	id PK · linked to workable_candidates
applications	`workable_applications`	id PK · linked to workable_candidates
interviews	`workable_interviews`	id PK · linked to workable_candidates

FAQ

How does Datrise handle Workable's custom fields in DuckDB?

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

How does the Workable to DuckDB sync stay up to date?

It runs incrementally — Datrise uses rewrites changed entities into the local database (or Parquet) on each run.

Related pipelines

Early access

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