DatriseAI-first ETL

FullStory MongoDB

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

How Datrise loads FullStory into MongoDB

Datrise syncs FullStory's sessions, events, funnels, frustration signals, and user properties 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

FullStory: Digital experience analytics with session replay context.

MongoDB: Document database for flexible schemas.

How FullStory entities map to MongoDB

FullStory entityMongoDB objectNotes
sessionsfullstory_sessionsid PK · custom fields → native nested documents
eventsfullstory_eventsBSON Date events
funnelsfullstory_funnelsid PK · linked to fullstory_sessions
frustration signalsfullstory_frustration_signalsid PK · linked to fullstory_sessions

FAQ

How does Datrise handle FullStory'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 FullStory 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

Early access

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