Iterable → GoodData
AI-first ETL from Iterable into GoodData. Governed entities, incremental sync, typed landing tables.
How Datrise loads Iterable into GoodData
Datrise syncs Iterable's users, campaigns, journeys, message events, and experiments into GoodData as warehouse tables GoodData maps into its logical data model. Flexible or custom fields land in flattened columns, and timestamps such as created, updated, and status changes are typed as date dimensions.
Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned facts. GoodData's LDM maps datasets by keys, so Datrise lands stable primary and foreign id columns to keep the model valid.
Ideal for embedded, multi-tenant analytics.
Endpoints
Iterable: Cross-channel marketing automation and journeys.
GoodData: Composable analytics platform with headless BI and embedded dashboards.
How Iterable entities map to GoodData
| Iterable entity | GoodData 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 dimensions events |
FAQ
How does Datrise handle Iterable's custom fields in GoodData?
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 GoodData types.
How does the Iterable to GoodData sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables.
Related pipelines
More destinations for Iterable
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