Snowplow to Domo Integration
AI-first ETL from Snowplow into Domo. Governed entities, incremental sync, typed landing tables.
How Datrise loads Snowplow into Domo
Datrise syncs Snowplow's events, users, sessions, contexts, page views, and event schemas into Domo as datasets in Domo's cloud store via connector. Flexible or custom fields land in flattened columns for Magic ETL, and timestamps such as created, updated, and status changes are typed as date/time columns.
Sync is incremental: Datrise uses partitioned dataset updates rather than full replaces, so re-runs update only what changed. Domo dataset partitions keyed on load date. Domo stores its own copy of data, so Datrise sends incremental partitions to avoid re-uploading whole datasets.
Ideal for all-in-one cloud BI with built-in ETL.
Endpoints
Snowplow: Behavioral event pipeline with a strict schema.
Domo: Cloud BI platform combining data integration and executive dashboards.
How Snowplow entities map to Domo
| Snowplow entity | Domo object | Notes |
|---|---|---|
| events | snowplow_events | date/time columns events |
| users | snowplow_users | id PK · linked to snowplow_events |
| sessions | snowplow_sessions | id PK · linked to snowplow_events |
| contexts | snowplow_contexts | id PK · linked to snowplow_events |
FAQ
How does Datrise handle Snowplow's custom fields in Domo?
Flexible values are stored as flattened columns for Magic ETL, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Domo types.
How does the Snowplow to Domo sync stay up to date?
It runs incrementally — Datrise uses partitioned dataset updates rather than full replaces.
Related pipelines
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