DatriseAI-first ETL

GitLab Google BigQuery

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

How Datrise loads GitLab into Google BigQuery

Datrise syncs GitLab's projects, merge requests, pipelines, issues, and deployment events 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

GitLab: DevOps platform for repos, CI/CD, and issue tracking.

Google BigQuery: Serverless analytics warehouse on GCP.

How GitLab entities map to Google BigQuery

GitLab entityGoogle BigQuery objectNotes
projectsgitlab_projectsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
merge requestsgitlab_merge_requestsid PK · linked to gitlab_projects
pipelinesgitlab_pipelinesid PK · linked to gitlab_projects
issuesgitlab_issuesid PK · linked to gitlab_projects

FAQ

How does Datrise handle GitLab'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 GitLab 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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