Amazon S3 → Amazon Redshift
AI-first ETL from Amazon S3 into Amazon Redshift. Governed entities, incremental sync, typed landing tables.
How Datrise loads Amazon S3 into Amazon Redshift
Datrise syncs Amazon S3's records, events, and configuration objects 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
Amazon S3: SaaS or API data source for analytics and warehouse sync.
Amazon Redshift: AWS petabyte-scale warehouse with Spectrum.
How Amazon S3 entities map to Amazon Redshift
| Amazon S3 entity | Amazon Redshift object | Notes |
|---|---|---|
| records | s3_records | id PK · custom fields → SUPER columns |
| events | s3_events | TIMESTAMPTZ events |
| configuration objects | s3_configuration_objects | id PK · linked to s3_records |
FAQ
How does Datrise handle Amazon S3'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 Amazon S3 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.
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Early access
Connect Amazon S3 to Amazon Redshift the easy way
Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.