DatriseAI-first ETL

Workable to Sisense Integration

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

How Datrise loads Workable into Sisense

Datrise syncs Workable's candidates, jobs, applications, interviews, offers, and pipeline stages into Sisense as modeled tables for a Sisense ElastiCube (or live connection). Flexible or custom fields land in flattened columns for the cube, and timestamps such as created, updated, and status changes are typed as date/time fields.

Sync is incremental: Datrise uses incremental ElastiCube builds on changed rows, so re-runs update only what changed. Date-partitioned facts to speed cube builds. ElastiCube is an in-memory model, so Datrise lands incremental, build-friendly tables rather than forcing full rebuilds.

Ideal for embedded analytics on an in-memory engine.

Endpoints

Workable: Recruiting pipeline and candidate evaluation.

Sisense: Analytics platform with elastic data models and embedded analytics.

How Workable entities map to Sisense

Workable entitySisense objectNotes
candidatesworkable_candidatesid PK · custom fields → flattened columns for the cube
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 Sisense?

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

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

It runs incrementally — Datrise uses incremental ElastiCube builds on changed rows.

Related pipelines

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