Iterable → Chartio
AI-first ETL from Iterable into Chartio. Governed entities, incremental sync, typed landing tables.
How Datrise loads Iterable into Chartio
Datrise syncs Iterable's users, campaigns, journeys, message events, and experiments into Chartio as SQL tables a visual-SQL explorer connects to. Flexible or custom fields land in flattened columns for visual SQL, and timestamps such as created, updated, and status changes are typed as temporal columns.
Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned facts. Visual-SQL tools build joins from your schema, so Datrise lands clearly related tables with stable id columns.
Ideal for drag-and-drop charting over a database.
Endpoints
Iterable: Cross-channel marketing automation and journeys.
Chartio: Cloud BI for exploring warehouse data with drag-and-drop charts.
How Iterable entities map to Chartio
| Iterable entity | Chartio object | Notes |
|---|---|---|
| users | iterable_users | id PK · custom fields → flattened columns for visual SQL |
| campaigns | iterable_campaigns | id PK · linked to iterable_users |
| journeys | iterable_journeys | id PK · linked to iterable_users |
| message events | iterable_message_events | temporal columns events |
FAQ
How does Datrise handle Iterable's custom fields in Chartio?
Flexible values are stored as flattened columns for visual SQL, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Chartio types.
How does the Iterable to Chartio sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables.
Related pipelines
More destinations for Iterable
Early access
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