Jobber to Google BigQuery Integration

How Datrise loads Jobber into Google BigQuery

Datrise syncs Jobber's jobs, clients, quotes, invoices, visits, and expenses 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

Jobber: Field service quoting, scheduling, and job tracking.

Google BigQuery: Serverless analytics warehouse on GCP.

How Jobber entities map to Google BigQuery

Jobber entity	Google BigQuery object	Notes
jobs	`jobber_jobs`	id PK · custom fields → JSON or nested/repeated (STRUCT) columns
clients	`jobber_clients`	id PK · linked to jobber_jobs
quotes	`jobber_quotes`	id PK · linked to jobber_jobs
invoices	`jobber_invoices`	id PK · linked to jobber_jobs

FAQ

How does Datrise handle Jobber'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 Jobber 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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