Snowplow → Looker
AI-first ETL from Snowplow into Looker. Governed entities, incremental sync, typed landing tables.
How Datrise loads Snowplow into Looker
Datrise syncs Snowplow's records, events, and configuration objects into Looker as governed warehouse tables with LookML-ready naming. Flexible or custom fields land in flattened columns (nested fields expanded for modeling), and timestamps such as created, updated, and status changes are typed as date/time dimension columns.
Sync is incremental: Datrise uses incremental refresh of the underlying warehouse tables Looker explores, so re-runs update only what changed. Date-partitioned fact tables for PDT performance. Looker models live in LookML on top of SQL, so Datrise lands clean, stable column names rather than churn that would break your views.
Ideal for governed, version-controlled BI on a warehouse.
Endpoints
Snowplow: SaaS or API data source for analytics and warehouse sync.
Looker: Google Cloud BI with LookML semantic models and governed dashboards.
How Snowplow entities map to Looker
| Snowplow entity | Looker object | Notes |
|---|---|---|
| records | snowplow_records | id PK · custom fields → flattened columns (nested fields expanded for modeling) |
| events | snowplow_events | date/time dimension columns events |
| configuration objects | snowplow_configuration_objects | id PK · linked to snowplow_records |
FAQ
How does Datrise handle Snowplow's custom fields in Looker?
Flexible values are stored as flattened columns (nested fields expanded for modeling), so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Looker types.
How does the Snowplow to Looker sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the underlying warehouse tables Looker explores.
Related pipelines
More destinations for Snowplow
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