Iterable → PlanetScale
AI-first ETL from Iterable into PlanetScale. Governed entities, incremental sync, typed landing tables.
How Datrise loads Iterable into PlanetScale
Datrise syncs Iterable's users, campaigns, journeys, message events, and experiments into PlanetScale as a typed table per source entity. Flexible or custom fields land in JSON columns, and timestamps such as created, updated, and status changes are typed as DATETIME.
Sync is incremental: Datrise uses a watermark on updated-at, applied with INSERT … ON DUPLICATE KEY UPDATE, so re-runs update only what changed. Vitess sharding by tenant or entity key for very large tables. PlanetScale disallows foreign-key constraints by default, so Datrise models relationships by stable id columns rather than enforced FKs.
Ideal for horizontally scalable MySQL apps on Vitess.
Endpoints
Iterable: Cross-channel marketing automation and journeys.
PlanetScale: Serverless MySQL platform with safe schema workflows.
How Iterable entities map to PlanetScale
| Iterable entity | PlanetScale object | Notes |
|---|---|---|
| users | iterable_users | id PK · custom fields → JSON columns |
| campaigns | iterable_campaigns | id PK · linked to iterable_users |
| journeys | iterable_journeys | id PK · linked to iterable_users |
| message events | iterable_message_events | DATETIME events |
FAQ
How does Datrise handle Iterable's custom fields in PlanetScale?
Flexible values are stored as JSON columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native PlanetScale types.
How does the Iterable to PlanetScale sync stay up to date?
It runs incrementally — Datrise uses a watermark on updated-at, applied with INSERT … ON DUPLICATE KEY UPDATE.
Related pipelines
More destinations for Iterable
More sources for PlanetScale
Early access
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