Pendo → Looker
AI-first ETL from Pendo into Looker. Governed entities, incremental sync, typed landing tables.
How Datrise loads Pendo into Looker
Datrise syncs Pendo's events, guides, NPS, feature adoption, and account metadata 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
Pendo: Product analytics and in-app guidance for SaaS teams.
Looker: Google Cloud BI with LookML semantic models and governed dashboards.
How Pendo entities map to Looker
| Pendo entity | Looker object | Notes |
|---|---|---|
| events | pendo_events | date/time dimension columns events |
| guides | pendo_guides | id PK · linked to pendo_events |
| NPS | pendo_nps | id PK · linked to pendo_events |
| feature adoption | pendo_feature_adoption | id PK · linked to pendo_events |
FAQ
How does Datrise handle Pendo'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 Pendo to Looker sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the underlying warehouse tables Looker explores.
Related pipelines
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Early access
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