DatriseAI-first ETL

Amazon DynamoDB to Amazon Redshift Integration

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

How Datrise loads Amazon DynamoDB into Amazon Redshift

Datrise syncs Amazon DynamoDB's tables, items, partition keys, sort keys, attributes, and stream records 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 DynamoDB: Serverless key-value and document store on AWS.

Amazon Redshift: AWS petabyte-scale warehouse with Spectrum.

How Amazon DynamoDB entities map to Amazon Redshift

Amazon DynamoDB entityAmazon Redshift objectNotes
tablessource_dynamodb_tablesid PK · custom fields → SUPER columns
itemssource_dynamodb_itemsid PK · linked to source_dynamodb_tables
partition keyssource_dynamodb_partition_keysid PK · linked to source_dynamodb_tables
sort keyssource_dynamodb_sort_keysid PK · linked to source_dynamodb_tables

FAQ

How does Datrise handle Amazon DynamoDB'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 DynamoDB 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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