DatriseAI-first ETL

Amazon Amazon S3 Amazon Redshift

AI-first ETL from Amazon Amazon S3 into Amazon Redshift. Governed entities, incremental sync, typed landing tables.

How Datrise loads Amazon Amazon S3 into Amazon Redshift

Datrise syncs Amazon 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 Amazon S3: SaaS or API data source for analytics and warehouse sync.

Amazon Redshift: AWS petabyte-scale warehouse with Spectrum.

How Amazon Amazon S3 entities map to Amazon Redshift

Amazon Amazon S3 entityAmazon Redshift objectNotes
recordsamazon_s3_recordsid PK · custom fields → SUPER columns
eventsamazon_s3_eventsTIMESTAMPTZ events
configuration objectsamazon_s3_configuration_objectsid PK · linked to amazon_s3_records

FAQ

How does Datrise handle Amazon 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 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.

Related pipelines

Early access

Connect Amazon Amazon S3 to Amazon Redshift the easy way

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