DatriseAI-first ETL

Amazon RDS to Amazon Redshift Integration

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

How Datrise loads Amazon RDS into Amazon Redshift

Datrise syncs Amazon RDS's tables, rows, schemas, indexes, primary keys, and change streams 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 RDS: Managed relational database service on AWS.

Amazon Redshift: AWS petabyte-scale warehouse with Spectrum.

How Amazon RDS entities map to Amazon Redshift

Amazon RDS entityAmazon Redshift objectNotes
tablesamazon_rds_tablesid PK · custom fields → SUPER columns
rowsamazon_rds_rowsid PK · linked to amazon_rds_tables
schemasamazon_rds_schemasid PK · linked to amazon_rds_tables
indexesamazon_rds_indexesid PK · linked to amazon_rds_tables

FAQ

How does Datrise handle Amazon RDS'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 RDS 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

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