DatriseAI-first ETL

SonarCloud to Looker Integration

AI-first ETL from SonarCloud into Looker. Governed entities, incremental sync, typed landing tables.

How Datrise loads SonarCloud into Looker

Datrise syncs SonarCloud's projects, issues, code smells, security hotspots, quality gates, and measures 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

SonarCloud: Static code quality and security analysis.

Looker: Google Cloud BI with LookML semantic models and governed dashboards.

How SonarCloud entities map to Looker

SonarCloud entityLooker objectNotes
projectssonar_cloud_projectsid PK · custom fields → flattened columns (nested fields expanded for modeling)
issuessonar_cloud_issuesid PK · linked to sonar_cloud_projects
code smellssonar_cloud_code_smellsid PK · linked to sonar_cloud_projects
security hotspotssonar_cloud_security_hotspotsid PK · linked to sonar_cloud_projects

FAQ

How does Datrise handle SonarCloud'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 SonarCloud to Looker sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the underlying warehouse tables Looker explores.

Related pipelines

Early access

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