Auth0 → Looker
AI-first ETL from Auth0 into Looker. Governed entities, incremental sync, typed landing tables.
How Datrise loads Auth0 into Looker
Datrise syncs Auth0's authentication logs, sign-ins, user identity changes, and security events 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
Auth0: Identity source for authentication and sign-in telemetry.
Looker: Google Cloud BI with LookML semantic models and governed dashboards.
How Auth0 entities map to Looker
| Auth0 entity | Looker object | Notes |
|---|---|---|
| authentication logs | auth0_authentication_logs | id PK · custom fields → flattened columns (nested fields expanded for modeling) |
| sign-ins | auth0_sign_ins | id PK · linked to auth0_authentication_logs |
| user identity changes | auth0_user_identity_changes | date/time dimension columns events |
| security events | auth0_security_events | date/time dimension columns events |
FAQ
How does Datrise handle Auth0'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 Auth0 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 Auth0
Early access
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