DatriseAI-first ETL

Clay to MongoDB Integration

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

How Datrise loads Clay into MongoDB

Datrise syncs Clay's tables, rows, enrichment runs, waterfalls, data sources, and exported lists 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

Clay: Data enrichment tables that build outbound lists.

MongoDB: Document database for flexible schemas.

How Clay entities map to MongoDB

Clay entityMongoDB objectNotes
tablesclay_tablesid PK · custom fields → native nested documents
rowsclay_rowsid PK · linked to clay_tables
enrichment runsclay_enrichment_runsid PK · linked to clay_tables
waterfallsclay_waterfallsid PK · linked to clay_tables

FAQ

How does Datrise handle Clay'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 Clay 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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