DatriseAI-first ETL

MongoDB Amazon Redshift

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

How Datrise loads MongoDB into Amazon Redshift

Datrise syncs MongoDB's collections, documents, change streams, and schema snapshots into Amazon Redshift as a typed table per source entity. Flexible or custom fields land in SUPER columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMPTZ.

Sync is incremental: Datrise uses COPY from staged files, then a delete-and-insert merge on stable id, so re-runs update only what changed. A DISTKEY on the join id and a SORTKEY on the load timestamp. Redshift performance hinges on dist/sort keys, so Datrise picks them from your entity ids and sync timestamps rather than defaulting to EVEN distribution.

Ideal for AWS-native warehouses already using the Redshift ecosystem.

Endpoints

MongoDB: Document database often used as an operational source for analytics.

Amazon Redshift: AWS petabyte-scale warehouse with Spectrum.

How MongoDB entities map to Amazon Redshift

MongoDB entityAmazon Redshift objectNotes
collectionsmongodb_collectionsid PK · custom fields → SUPER columns
documentsmongodb_documentsid PK · linked to mongodb_collections
change streamsmongodb_change_streamsTIMESTAMPTZ events
schema snapshotsmongodb_schema_snapshotsid PK · linked to mongodb_collections

FAQ

How does Datrise handle MongoDB's custom fields in Amazon Redshift?

Flexible values are stored as SUPER columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon Redshift types.

How does the MongoDB to Amazon Redshift sync stay up to date?

It runs incrementally — Datrise uses COPY from staged files, then a delete-and-insert merge on stable id.

Related pipelines

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