DatriseAI-first ETL

Salesforce Pardot to MongoDB Integration

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

How Datrise loads Salesforce Pardot into MongoDB

Datrise syncs Salesforce Pardot's prospects, campaigns, list emails, forms, lead scores, and engagement history 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

Salesforce Pardot: B2B marketing automation scoring and nurturing leads.

MongoDB: Document database for flexible schemas.

How Salesforce Pardot entities map to MongoDB

Salesforce Pardot entityMongoDB objectNotes
prospectssalesforce_pardot_prospectsid PK · custom fields → native nested documents
campaignssalesforce_pardot_campaignsid PK · linked to salesforce_pardot_prospects
list emailssalesforce_pardot_list_emailsid PK · linked to salesforce_pardot_prospects
formssalesforce_pardot_formsid PK · linked to salesforce_pardot_prospects

FAQ

How does Datrise handle Salesforce Pardot'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 Salesforce Pardot 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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