Iterable → Microsoft SQL Server
AI-first ETL from Iterable into Microsoft SQL Server. Governed entities, incremental sync, typed landing tables.
How Datrise loads Iterable into Microsoft SQL Server
Datrise syncs Iterable's users, campaigns, journeys, message events, and experiments into Microsoft SQL Server as a typed table per source entity. Flexible or custom fields land in NVARCHAR(MAX) JSON columns, and timestamps such as created, updated, and status changes are typed as datetime2.
Sync is incremental: Datrise uses a watermark on updated-at, applied with a MERGE statement, so re-runs update only what changed. Optional partitioned tables on a date partition function. SQL Server defaults to a case-insensitive collation, so Datrise preserves original casing in a metadata column to avoid silent key collisions.
Ideal for Microsoft-stack analytics and Power BI Import models.
Endpoints
Iterable: Cross-channel marketing automation and journeys.
Microsoft SQL Server: Microsoft relational DB with enterprise features.
How Iterable entities map to Microsoft SQL Server
| Iterable entity | Microsoft SQL Server object | Notes |
|---|---|---|
| users | iterable_users | id PK · custom fields → NVARCHAR(MAX) 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 | datetime2 events |
FAQ
How does Datrise handle Iterable's custom fields in Microsoft SQL Server?
Flexible values are stored as NVARCHAR(MAX) JSON columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Microsoft SQL Server types.
How does the Iterable to Microsoft SQL Server sync stay up to date?
It runs incrementally — Datrise uses a watermark on updated-at, applied with a MERGE statement.
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Early access
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