Amplitude → Looker
AI-first ETL from Amplitude into Looker. Governed entities, incremental sync, typed landing tables.
How Datrise loads Amplitude into Looker
Datrise syncs Amplitude's product events, user properties, funnels, cohorts, and retention curves 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
Amplitude: Product analytics source for events, funnels, and cohorts.
Looker: Google Cloud BI with LookML semantic models and governed dashboards.
How Amplitude entities map to Looker
| Amplitude entity | Looker object | Notes |
|---|---|---|
| product events | amplitude_product_events | date/time dimension columns events |
| user properties | amplitude_user_properties | id PK · linked to amplitude_product_events |
| funnels | amplitude_funnels | id PK · linked to amplitude_product_events |
| cohorts | amplitude_cohorts | id PK · linked to amplitude_product_events |
FAQ
How does Datrise handle Amplitude'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 Amplitude 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 Amplitude
Early access
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