DatriseAI-first ETL

Ashby to Google BigQuery Integration

AI-first ETL from Ashby into Google BigQuery. Governed entities, incremental sync, typed landing tables.

How Datrise loads Ashby into Google BigQuery

Datrise syncs Ashby's candidates, applications, jobs, interviews, offers, and interview feedback into Google BigQuery as a partitioned table per source entity. Flexible or custom fields land in JSON or nested/repeated (STRUCT) columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target, so re-runs update only what changed. Partition by ingestion or event date and cluster by entity id to keep scanned bytes low. BigQuery bills by bytes scanned, so Datrise partitions and clusters every table to keep query costs predictable.

Ideal for Google-stack analytics and ML on serverless infrastructure.

Endpoints

Ashby: Applicant tracking and recruiting analytics.

Google BigQuery: Serverless analytics warehouse on GCP.

How Ashby entities map to Google BigQuery

Ashby entityGoogle BigQuery objectNotes
candidatesashby_candidatesid PK · custom fields → JSON or nested/repeated (STRUCT) columns
applicationsashby_applicationsid PK · linked to ashby_candidates
jobsashby_jobsid PK · linked to ashby_candidates
interviewsashby_interviewsid PK · linked to ashby_candidates

FAQ

How does Datrise handle Ashby's custom fields in Google BigQuery?

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

How does the Ashby to Google BigQuery sync stay up to date?

It runs incrementally — Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target.

Related pipelines

Early access

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