DatriseAI-first ETL

Jobber Looker Studio

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

How Datrise loads Jobber into Looker Studio

Datrise syncs Jobber's contacts, accounts, deals, activities, and lifecycle events 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

Jobber: Field service CRM for scheduling, jobs, and customer history.

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

How Jobber entities map to Looker Studio

Jobber entityLooker Studio objectNotes
contactsjobber_contactsid PK · custom fields → flattened columns for chart fields
accountsjobber_accountsid PK · linked to jobber_contacts
dealsjobber_dealsid PK · linked to jobber_contacts
activitiesjobber_activitiesdate dimension columns events

FAQ

How does Datrise handle Jobber'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 Jobber 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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