DatriseAI-first ETL

JobAdder to Google BigQuery Integration

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

How Datrise loads JobAdder into Google BigQuery

Datrise syncs JobAdder's candidates, jobs, applications, placements, companies, and interview notes 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

JobAdder: Recruitment platform for job ads, applications, and placements.

Google BigQuery: Serverless analytics warehouse on GCP.

How JobAdder entities map to Google BigQuery

JobAdder entityGoogle BigQuery objectNotes
candidatesjobadder_candidatesid PK · custom fields → JSON or nested/repeated (STRUCT) columns
jobsjobadder_jobsid PK · linked to jobadder_candidates
applicationsjobadder_applicationsid PK · linked to jobadder_candidates
placementsjobadder_placementsid PK · linked to jobadder_candidates

FAQ

How does Datrise handle JobAdder'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 JobAdder 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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