Redshift to MongoDB Integration
AI-first ETL from Redshift into MongoDB. Governed entities, incremental sync, typed landing tables.
How Datrise loads Redshift into MongoDB
Datrise syncs Redshift's tables, rows, schemas, sort keys, distribution keys, and query 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
Redshift: Columnar cloud data warehouse on AWS.
MongoDB: Document database for flexible schemas.
How Redshift entities map to MongoDB
| Redshift entity | MongoDB object | Notes |
|---|---|---|
| tables | redshift_tables | id PK · custom fields → native nested documents |
| rows | redshift_rows | id PK · linked to redshift_tables |
| schemas | redshift_schemas | id PK · linked to redshift_tables |
| sort keys | redshift_sort_keys | id PK · linked to redshift_tables |
FAQ
How does Datrise handle Redshift'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 Redshift 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 Redshift
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