DatriseAI-first ETL

Jobber Tableau

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

How Datrise loads Jobber into Tableau

Datrise syncs Jobber's contacts, accounts, deals, activities, and lifecycle events into Tableau as warehouse tables or a refreshed .hyper extract. Flexible or custom fields land in flattened columns for Tableau fields, and timestamps such as created, updated, and status changes are typed as date/datetime fields.

Sync is incremental: Datrise uses incremental refresh of the tables behind a live connection or extract, so re-runs update only what changed. Date-partitioned facts to keep extract refresh quick. Tableau .hyper extracts snapshot data, so Datrise keeps the source tables incremental and lets you choose live vs extract.

Ideal for visual analytics and dashboards in Tableau.

Endpoints

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

Tableau: Salesforce analytics platform for interactive dashboards and visual exploration.

How Jobber entities map to Tableau

Jobber entityTableau objectNotes
contactsjobber_contactsid PK · custom fields → flattened columns for Tableau fields
accountsjobber_accountsid PK · linked to jobber_contacts
dealsjobber_dealsid PK · linked to jobber_contacts
activitiesjobber_activitiesdate/datetime fields events

FAQ

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

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

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

It runs incrementally — Datrise uses incremental refresh of the tables behind a live connection or extract.

Related pipelines

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