DatriseAI-first ETL

Gainsight to MongoDB Integration

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

How Datrise loads Gainsight into MongoDB

Datrise syncs Gainsight's customers, health scores, playbooks, tasks, surveys, and usage metrics 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

Gainsight: Customer success health scoring and playbooks.

MongoDB: Document database for flexible schemas.

How Gainsight entities map to MongoDB

Gainsight entityMongoDB objectNotes
customersgainsight_customersid PK · custom fields → native nested documents
health scoresgainsight_health_scoresid PK · linked to gainsight_customers
playbooksgainsight_playbooksid PK · linked to gainsight_customers
tasksgainsight_tasksid PK · linked to gainsight_customers

FAQ

How does Datrise handle Gainsight'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 Gainsight 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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