DatriseAI-first ETL

Aws Cloudtrail Neon

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

How Datrise loads Aws Cloudtrail into Neon

Datrise syncs Aws Cloudtrail'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

Aws Cloudtrail: SaaS or API data source for analytics and warehouse sync.

Neon: Serverless Postgres destination with branching and autoscaling.

How Aws Cloudtrail entities map to Neon

Aws Cloudtrail entityNeon objectNotes
recordsaws_cloudtrail_recordsid PK · custom fields → jsonb columns
eventsaws_cloudtrail_eventstimestamptz events
configuration objectsaws_cloudtrail_configuration_objectsid PK · linked to aws_cloudtrail_records

FAQ

How does Datrise handle Aws Cloudtrail'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 Aws Cloudtrail 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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