Iterable → Yellowfin
AI-first ETL from Iterable into Yellowfin. Governed entities, incremental sync, typed landing tables.
How Datrise loads Iterable into Yellowfin
Datrise syncs Iterable's users, campaigns, journeys, message events, and experiments into Yellowfin as warehouse tables Yellowfin builds views on. Flexible or custom fields land in flattened columns, and timestamps such as created, updated, and status changes are typed as date/time dimensions.
Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned facts. Yellowfin views reference columns by name, so Datrise lands stable, well-typed columns to keep reports valid.
Ideal for dashboards with automated data storytelling.
Endpoints
Iterable: Cross-channel marketing automation and journeys.
Yellowfin: BI suite with dashboards, automated insights, and data storytelling.
How Iterable entities map to Yellowfin
| Iterable entity | Yellowfin object | Notes |
|---|---|---|
| users | iterable_users | id PK · custom fields → flattened columns |
| campaigns | iterable_campaigns | id PK · linked to iterable_users |
| journeys | iterable_journeys | id PK · linked to iterable_users |
| message events | iterable_message_events | date/time dimensions events |
FAQ
How does Datrise handle Iterable's custom fields in Yellowfin?
Flexible values are stored as flattened columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Yellowfin types.
How does the Iterable to Yellowfin sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables.
Related pipelines
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