Iterable → Amazon Redshift
AI-first ETL from Iterable into Amazon Redshift. Governed entities, incremental sync, typed landing tables.
How Datrise loads Iterable into Amazon Redshift
Datrise syncs Iterable's users, campaigns, journeys, message events, and experiments into Amazon Redshift as a typed table per source entity. Flexible or custom fields land in SUPER columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMPTZ.
Sync is incremental: Datrise uses COPY from staged files, then a delete-and-insert merge on stable id, so re-runs update only what changed. A DISTKEY on the join id and a SORTKEY on the load timestamp. Redshift performance hinges on dist/sort keys, so Datrise picks them from your entity ids and sync timestamps rather than defaulting to EVEN distribution.
Ideal for AWS-native warehouses already using the Redshift ecosystem.
Endpoints
Iterable: Cross-channel marketing automation and journeys.
Amazon Redshift: AWS petabyte-scale warehouse with Spectrum.
How Iterable entities map to Amazon Redshift
| Iterable entity | Amazon Redshift object | Notes |
|---|---|---|
| users | iterable_users | id PK · custom fields → SUPER 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 Amazon Redshift?
Flexible values are stored as SUPER columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon Redshift types.
How does the Iterable to Amazon Redshift sync stay up to date?
It runs incrementally — Datrise uses COPY from staged files, then a delete-and-insert merge on stable id.
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Early access
Connect Iterable to Amazon Redshift the easy way
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