DatriseAI-first ETL

Amazon Rds Amazon DynamoDB

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

How Datrise loads Amazon Rds into Amazon DynamoDB

Datrise syncs Amazon Rds's records, events, and configuration objects into Amazon DynamoDB as an item per source record in a table per entity. Flexible or custom fields land in nested map/list attributes, and timestamps such as created, updated, and status changes are typed as ISO-8601 string or epoch number attributes.

Sync is incremental: Datrise uses PutItem/UpdateItem keyed on a partition key derived from the entity id, so re-runs update only what changed. Partition-key design on the entity id to spread throughput evenly. DynamoDB rewards access-pattern-first key design, so Datrise sets partition/sort keys from your entity ids rather than scan-heavy defaults.

Ideal for serverless apps needing single-digit-millisecond key lookups on CRM data.

Endpoints

Amazon Rds: SaaS or API data source for analytics and warehouse sync.

Amazon DynamoDB: Serverless key-value and document store on AWS.

How Amazon Rds entities map to Amazon DynamoDB

Amazon Rds entityAmazon DynamoDB objectNotes
recordsamazon_rds_recordsid PK · custom fields → nested map/list attributes
eventsamazon_rds_eventsISO-8601 string or epoch number attributes events
configuration objectsamazon_rds_configuration_objectsid PK · linked to amazon_rds_records

FAQ

How does Datrise handle Amazon Rds's custom fields in Amazon DynamoDB?

Flexible values are stored as nested map/list attributes, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon DynamoDB types.

How does the Amazon Rds to Amazon DynamoDB sync stay up to date?

It runs incrementally — Datrise uses PutItem/UpdateItem keyed on a partition key derived from the entity id.

Related pipelines

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