DatriseAI-first ETL

SonarCloud to Google BigQuery Integration

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

How Datrise loads SonarCloud into Google BigQuery

Datrise syncs SonarCloud's projects, issues, code smells, security hotspots, quality gates, and measures into Google BigQuery as a partitioned table per source entity. Flexible or custom fields land in JSON or nested/repeated (STRUCT) columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target, so re-runs update only what changed. Partition by ingestion or event date and cluster by entity id to keep scanned bytes low. BigQuery bills by bytes scanned, so Datrise partitions and clusters every table to keep query costs predictable.

Ideal for Google-stack analytics and ML on serverless infrastructure.

Endpoints

SonarCloud: Static code quality and security analysis.

Google BigQuery: Serverless analytics warehouse on GCP.

How SonarCloud entities map to Google BigQuery

SonarCloud entityGoogle BigQuery objectNotes
projectssonar_cloud_projectsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
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 Google BigQuery?

Flexible values are stored as JSON or nested/repeated (STRUCT) columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Google BigQuery types.

How does the SonarCloud to Google BigQuery sync stay up to date?

It runs incrementally — Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target.

Related pipelines

Early access

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