DatriseAI-first ETL

Practifi MongoDB

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

How Datrise loads Practifi into MongoDB

Datrise syncs Practifi's contacts, accounts, deals, activities, and lifecycle events 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

Practifi: Financial advisor CRM for clients, households, and compliance workflows.

MongoDB: Document database for flexible schemas.

How Practifi entities map to MongoDB

Practifi entityMongoDB objectNotes
contactspractifi_contactsid PK · custom fields → native nested documents
accountspractifi_accountsid PK · linked to practifi_contacts
dealspractifi_dealsid PK · linked to practifi_contacts
activitiespractifi_activitiesBSON Date events

FAQ

How does Datrise handle Practifi'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 Practifi 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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