CSV File to MongoDB Integration
AI-first ETL from CSV File into MongoDB. Governed entities, incremental sync, typed landing tables.
How Datrise loads CSV File into MongoDB
Datrise syncs CSV File's rows, columns, headers, delimiters, file batches, and encodings 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
CSV File: Delimited flat files from exports and drops.
MongoDB: Document database for flexible schemas.
How CSV File entities map to MongoDB
| CSV File entity | MongoDB object | Notes |
|---|---|---|
| rows | csv_file_rows | id PK · custom fields → native nested documents |
| columns | csv_file_columns | id PK · linked to csv_file_rows |
| headers | csv_file_headers | id PK · linked to csv_file_rows |
| delimiters | csv_file_delimiters | id PK · linked to csv_file_rows |
FAQ
How does Datrise handle CSV File'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 CSV File 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
More destinations for CSV File
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