DatriseAI-first ETL

Zendesk Chat MongoDB

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

How Datrise loads Zendesk Chat into MongoDB

Datrise syncs Zendesk Chat's chats, agents, visitors, departments, and response times 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

Zendesk Chat: Live chat conversations and agent performance.

MongoDB: Document database for flexible schemas.

How Zendesk Chat entities map to MongoDB

Zendesk Chat entityMongoDB objectNotes
chatszendesk_chat_chatsid PK · custom fields → native nested documents
agentszendesk_chat_agentsid PK · linked to zendesk_chat_chats
visitorszendesk_chat_visitorsid PK · linked to zendesk_chat_chats
departmentszendesk_chat_departmentsid PK · linked to zendesk_chat_chats

FAQ

How does Datrise handle Zendesk Chat'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 Zendesk Chat 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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