Pagerduty to Looker Integration
AI-first ETL from Pagerduty into Looker. Governed entities, incremental sync, typed landing tables.
How Datrise loads Pagerduty into Looker
Datrise syncs Pagerduty's incidents, services, schedules, escalation policies, log entries, and responders 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
Pagerduty: Incident response, on-call schedules, and escalation.
Looker: Google Cloud BI with LookML semantic models and governed dashboards.
How Pagerduty entities map to Looker
| Pagerduty entity | Looker object | Notes |
|---|---|---|
| incidents | pagerduty_incidents | id PK · custom fields → flattened columns (nested fields expanded for modeling) |
| services | pagerduty_services | id PK · linked to pagerduty_incidents |
| schedules | pagerduty_schedules | id PK · linked to pagerduty_incidents |
| escalation policies | pagerduty_escalation_policies | id PK · linked to pagerduty_incidents |
FAQ
How does Datrise handle Pagerduty'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 Pagerduty to Looker sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the underlying warehouse tables Looker explores.
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