Snowplow to Looker Studio Integration
AI-first ETL from Snowplow into Looker Studio. Governed entities, incremental sync, typed landing tables.
How Datrise loads Snowplow into Looker Studio
Datrise syncs Snowplow's events, users, sessions, contexts, page views, and event schemas into Looker Studio as warehouse tables Looker Studio connects to. Flexible or custom fields land in flattened columns for chart fields, and timestamps such as created, updated, and status changes are typed as date dimension columns.
Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned tables to keep extract refresh fast. Looker Studio performs best on pre-aggregated tables, so Datrise lands tidy, report-shaped tables rather than raw API payloads.
Ideal for free, shareable dashboards on Google data sources.
Endpoints
Snowplow: Behavioral event pipeline with a strict schema.
Looker Studio: Google self-service dashboards and reporting (formerly Data Studio).
How Snowplow entities map to Looker Studio
| Snowplow entity | Looker Studio object | Notes |
|---|---|---|
| events | snowplow_events | date dimension 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 Looker Studio?
Flexible values are stored as flattened columns for chart fields, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Looker Studio types.
How does the Snowplow to Looker Studio sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables.
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Early access
Connect Snowplow to Looker Studio the easy way
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