Amazon S3 to MongoDB Integration
AI-first ETL from Amazon S3 into MongoDB. Governed entities, incremental sync, typed landing tables.
How Datrise loads Amazon S3 into MongoDB
Datrise syncs Amazon S3's buckets, objects, prefixes, object metadata, versions, and storage classes 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
Amazon S3: Object storage for files, exports, and data lakes.
MongoDB: Document database for flexible schemas.
How Amazon S3 entities map to MongoDB
| Amazon S3 entity | MongoDB object | Notes |
|---|---|---|
| buckets | s3_buckets | id PK · custom fields → native nested documents |
| objects | s3_objects | id PK · linked to s3_buckets |
| prefixes | s3_prefixes | id PK · linked to s3_buckets |
| object metadata | s3_object_metadata | id PK · linked to s3_buckets |
FAQ
How does Datrise handle Amazon S3'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 Amazon S3 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 Amazon S3
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