Amazon Rds → Neon
AI-first ETL from Amazon Rds into Neon. Governed entities, incremental sync, typed landing tables.
How Datrise loads Amazon Rds into Neon
Datrise syncs Amazon Rds's records, events, and configuration objects into Neon as a typed table per source entity. Flexible or custom fields land in jsonb columns, and timestamps such as created, updated, and status changes are typed as timestamptz.
Sync is incremental: Datrise uses a watermark on updated-at, applied with INSERT … ON CONFLICT DO UPDATE, so re-runs update only what changed. Optional declarative partitioning by load date. Neon separates compute from storage, so Datrise batches writes to keep autoscaling compute from cold-starting on every small change.
Ideal for serverless Postgres workloads that scale to zero between syncs.
Endpoints
Amazon Rds: SaaS or API data source for analytics and warehouse sync.
Neon: Serverless Postgres destination with branching and autoscaling.
How Amazon Rds entities map to Neon
| Amazon Rds entity | Neon object | Notes |
|---|---|---|
| records | amazon_rds_records | id PK · custom fields → jsonb columns |
| events | amazon_rds_events | timestamptz events |
| configuration objects | amazon_rds_configuration_objects | id PK · linked to amazon_rds_records |
FAQ
How does Datrise handle Amazon Rds's custom fields in Neon?
Flexible values are stored as jsonb columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Neon types.
How does the Amazon Rds to Neon sync stay up to date?
It runs incrementally — Datrise uses a watermark on updated-at, applied with INSERT … ON CONFLICT DO UPDATE.
Related pipelines
More destinations for Amazon Rds
Early access
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