DatriseAI-first ETL

Google Analytics MongoDB

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

How Datrise loads Google Analytics into MongoDB

Datrise syncs Google Analytics's sessions, events, channels, conversions, and behavior cohorts 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

Google Analytics: Web and product analytics for behavior and traffic insights.

MongoDB: Document database for flexible schemas.

How Google Analytics entities map to MongoDB

Google Analytics entityMongoDB objectNotes
sessionsgoogle_analytics_sessionsid PK · custom fields → native nested documents
eventsgoogle_analytics_eventsBSON Date events
channelsgoogle_analytics_channelsid PK · linked to google_analytics_sessions
conversionsgoogle_analytics_conversionsid PK · linked to google_analytics_sessions

FAQ

How does Datrise handle Google Analytics'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 Google Analytics 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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