Iterable → MongoDB
AI-first ETL from Iterable into MongoDB. Governed entities, incremental sync, typed landing tables.
How Datrise loads Iterable into MongoDB
Datrise syncs Iterable's users, campaigns, journeys, message events, and experiments 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
Iterable: Cross-channel marketing automation and journeys.
MongoDB: Document database for flexible schemas.
How Iterable entities map to MongoDB
| Iterable entity | MongoDB object | Notes |
|---|---|---|
| users | iterable_users | id PK · custom fields → native nested documents |
| campaigns | iterable_campaigns | id PK · linked to iterable_users |
| journeys | iterable_journeys | id PK · linked to iterable_users |
| message events | iterable_message_events | BSON Date events |
FAQ
How does Datrise handle Iterable'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 Iterable 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 Iterable
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