DatriseAI-first ETL

Amazon Amazon S3 Neon

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

How Datrise loads Amazon Amazon S3 into Neon

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

Neon: Serverless Postgres destination with branching and autoscaling.

How Amazon Amazon S3 entities map to Neon

Amazon Amazon S3 entityNeon objectNotes
recordsamazon_s3_recordsid PK · custom fields → jsonb 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 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 Amazon S3 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

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