DatriseAI-first ETL

Workable to Looker Studio Integration

AI-first ETL from Workable into Looker Studio. Governed entities, incremental sync, typed landing tables.

How Datrise loads Workable into Looker Studio

Datrise syncs Workable's candidates, jobs, applications, interviews, offers, and pipeline stages into Looker Studio as warehouse tables Looker Studio connects to. Flexible or custom fields land in flattened columns for chart fields, and timestamps such as created, updated, and status changes are typed as date dimension columns.

Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned tables to keep extract refresh fast. Looker Studio performs best on pre-aggregated tables, so Datrise lands tidy, report-shaped tables rather than raw API payloads.

Ideal for free, shareable dashboards on Google data sources.

Endpoints

Workable: Recruiting pipeline and candidate evaluation.

Looker Studio: Google self-service dashboards and reporting (formerly Data Studio).

How Workable entities map to Looker Studio

Workable entityLooker Studio objectNotes
candidatesworkable_candidatesid PK · custom fields → flattened columns for chart fields
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 Looker Studio?

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

How does the Workable to Looker Studio sync stay up to date?

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

Related pipelines

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