Jobber → ThoughtSpot
AI-first ETL from Jobber into ThoughtSpot. Governed entities, incremental sync, typed landing tables.
How Datrise loads Jobber into ThoughtSpot
Datrise syncs Jobber's contacts, accounts, deals, activities, and lifecycle events into ThoughtSpot as warehouse tables ThoughtSpot indexes for search. Flexible or custom fields land in flattened columns for searchable fields, and timestamps such as created, updated, and status changes are typed as date/time columns.
Sync is incremental: Datrise uses incremental refresh of the indexed tables, so re-runs update only what changed. Date-partitioned facts for live-query performance. ThoughtSpot search relies on clear names and relationships, so Datrise lands well-named, joinable tables.
Ideal for natural-language search analytics over a warehouse.
Endpoints
Jobber: Field service CRM for scheduling, jobs, and customer history.
ThoughtSpot: Search-driven analytics with AI-assisted insights on warehouse data.
How Jobber entities map to ThoughtSpot
| Jobber entity | ThoughtSpot object | Notes |
|---|---|---|
| contacts | jobber_contacts | id PK · custom fields → flattened columns for searchable fields |
| accounts | jobber_accounts | id PK · linked to jobber_contacts |
| deals | jobber_deals | id PK · linked to jobber_contacts |
| activities | jobber_activities | date/time columns events |
FAQ
How does Datrise handle Jobber's custom fields in ThoughtSpot?
Flexible values are stored as flattened columns for searchable fields, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native ThoughtSpot types.
How does the Jobber to ThoughtSpot sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the indexed tables.
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