Jobber → MongoDB
AI-first ETL from Jobber into MongoDB. Governed entities, incremental sync, typed landing tables.
How Datrise loads Jobber into MongoDB
Datrise syncs Jobber's contacts, accounts, deals, activities, and lifecycle events into MongoDB as a collection per source entity. Flexible or custom fields land in native nested documents, and timestamps such as created, updated, and status changes are typed as BSON Date.
Sync is incremental: Datrise uses upserts by stable id with updateOne(upsert) on the source primary key, so re-runs update only what changed. Optional sharding on the entity id for large collections. Mongo has no fixed schema, so Datrise keeps field types consistent across documents to avoid mixed-type query surprises.
Ideal for document-oriented apps that want CRM data in their existing Mongo store.
Endpoints
Jobber: Field service CRM for scheduling, jobs, and customer history.
MongoDB: Document database for flexible schemas.
How Jobber entities map to MongoDB
| Jobber entity | MongoDB object | Notes |
|---|---|---|
| contacts | jobber_contacts | id PK · custom fields → native nested documents |
| accounts | jobber_accounts | id PK · linked to jobber_contacts |
| deals | jobber_deals | id PK · linked to jobber_contacts |
| activities | jobber_activities | BSON Date events |
FAQ
How does Datrise handle Jobber's custom fields in MongoDB?
Flexible values are stored as native nested documents, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native MongoDB types.
How does the Jobber to MongoDB sync stay up to date?
It runs incrementally — Datrise uses upserts by stable id with updateOne(upsert) on the source primary key.
Related pipelines
More destinations for Jobber
Early access
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