DatriseAI-first ETL

Jobber Spotfire

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

How Datrise loads Jobber into Spotfire

Datrise syncs Jobber's contacts, accounts, deals, activities, and lifecycle events into Spotfire as warehouse tables or in-memory data for Spotfire analyses. Flexible or custom fields land in flattened columns for visualizations, and timestamps such as created, updated, and status changes are typed as date/time columns.

Sync is incremental: Datrise uses incremental refresh of the connected tables or in-memory data, so re-runs update only what changed. Date-partitioned facts. Spotfire can load data in-memory, so Datrise keeps the backing tables incremental so analyses refresh without full reloads.

Ideal for interactive analytical visualization and data science.

Endpoints

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

Spotfire: Visual analytics platform for interactive dashboards and data science workflows.

How Jobber entities map to Spotfire

Jobber entitySpotfire objectNotes
contactsjobber_contactsid PK · custom fields → flattened columns for visualizations
accountsjobber_accountsid PK · linked to jobber_contacts
dealsjobber_dealsid PK · linked to jobber_contacts
activitiesjobber_activitiesdate/time columns events

FAQ

How does Datrise handle Jobber's custom fields in Spotfire?

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

How does the Jobber to Spotfire sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the connected tables or in-memory data.

Related pipelines

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