auditing-warehouse-data-health

Run a data warehouse health audit to surface active failures across sources, schemas, materialized views, destinations, and transformations. It generates a prioritized, actionable report so teams know exactly where to start during weekly reviews or project onboarding.

The best way to triage broken materialized views and sync schemas is to retrieve health issues and cross-reference them with view run history and schema lists. This groups failures by type and prioritizes them, providing actionable next steps for targeted remediation.

Yes, you can check data warehouse health issues across multiple sources and destinations simultaneously. A single health scan retrieves all active failures and groups them by category, including external data sources, sync schemas, and batch export destinations.

PostHog dashboards show stale or missing warehouse data when underlying pipeline items fail. Auditing warehouse health surfaces these broken sources, sync schemas, or materialized views, helping you pinpoint the exact failure point disrupting your data flow.

No, the data warehouse health audit requires no external dependencies or components. It operates independently by querying internal endpoints like external-data-sources-list and view-run-history to assemble its prioritized health report.

Run a data warehouse health check during project onboarding to immediately identify broken or degraded pipeline items across sources, schemas, and transformations. This ensures new projects start with a clean baseline and prevents dashboard data issues later.