Iterable → Neon
AI-first ETL from Iterable into Neon. Governed entities, incremental sync, typed landing tables.
How Datrise loads Iterable into Neon
Datrise syncs Iterable's users, campaigns, journeys, message events, and experiments 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
Iterable: Cross-channel marketing automation and journeys.
Neon: Serverless Postgres destination with branching and autoscaling.
How Iterable entities map to Neon
| Iterable entity | Neon object | Notes |
|---|---|---|
| users | iterable_users | id PK · custom fields → jsonb columns |
| campaigns | iterable_campaigns | id PK · linked to iterable_users |
| journeys | iterable_journeys | id PK · linked to iterable_users |
| message events | iterable_message_events | timestamptz events |
FAQ
How does Datrise handle Iterable'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 Iterable 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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Early access
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