Iterable → Google BigQuery
AI-first ETL from Iterable into Google BigQuery. Governed entities, incremental sync, typed landing tables.
How Datrise loads Iterable into Google BigQuery
Datrise syncs Iterable's users, campaigns, journeys, message events, and experiments into Google BigQuery as a partitioned table per source entity. Flexible or custom fields land in JSON or nested/repeated (STRUCT) columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.
Sync is incremental: Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target, so re-runs update only what changed. Partition by ingestion or event date and cluster by entity id to keep scanned bytes low. BigQuery bills by bytes scanned, so Datrise partitions and clusters every table to keep query costs predictable.
Ideal for Google-stack analytics and ML on serverless infrastructure.
Endpoints
Iterable: Cross-channel marketing automation and journeys.
Google BigQuery: Serverless analytics warehouse on GCP.
How Iterable entities map to Google BigQuery
| Iterable entity | Google BigQuery object | Notes |
|---|---|---|
| users | iterable_users | id PK · custom fields → JSON or nested/repeated (STRUCT) columns |
| campaigns | iterable_campaigns | id PK · linked to iterable_users |
| journeys | iterable_journeys | id PK · linked to iterable_users |
| message events | iterable_message_events | TIMESTAMP events |
FAQ
How does Datrise handle Iterable's custom fields in Google BigQuery?
Flexible values are stored as JSON or nested/repeated (STRUCT) columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Google BigQuery types.
How does the Iterable to Google BigQuery sync stay up to date?
It runs incrementally — Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target.
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Early access
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