DatriseAI-first ETL

Fauna to MongoDB Integration

AI-first ETL from Fauna into MongoDB. Governed entities, incremental sync, typed landing tables.

How Datrise loads Fauna into MongoDB

Datrise syncs Fauna's collections, documents, indexes, functions, database schemas, and document history 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

Fauna: Distributed document-relational database.

MongoDB: Document database for flexible schemas.

How Fauna entities map to MongoDB

Fauna entityMongoDB objectNotes
collectionsfauna_collectionsid PK · custom fields → native nested documents
documentsfauna_documentsid PK · linked to fauna_collections
indexesfauna_indexesid PK · linked to fauna_collections
functionsfauna_functionsid PK · linked to fauna_collections

FAQ

How does Datrise handle Fauna'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 Fauna 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

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