DatriseAI-first ETL

GitLab Amazon Redshift

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

How Datrise loads GitLab into Amazon Redshift

Datrise syncs GitLab's projects, merge requests, pipelines, issues, and deployment events into Amazon Redshift as a typed table per source entity. Flexible or custom fields land in SUPER columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMPTZ.

Sync is incremental: Datrise uses COPY from staged files, then a delete-and-insert merge on stable id, so re-runs update only what changed. A DISTKEY on the join id and a SORTKEY on the load timestamp. Redshift performance hinges on dist/sort keys, so Datrise picks them from your entity ids and sync timestamps rather than defaulting to EVEN distribution.

Ideal for AWS-native warehouses already using the Redshift ecosystem.

Endpoints

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

Amazon Redshift: AWS petabyte-scale warehouse with Spectrum.

How GitLab entities map to Amazon Redshift

GitLab entityAmazon Redshift objectNotes
projectsgitlab_projectsid PK · custom fields → SUPER 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 Amazon Redshift?

Flexible values are stored as SUPER columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon Redshift types.

How does the GitLab to Amazon Redshift sync stay up to date?

It runs incrementally — Datrise uses COPY from staged files, then a delete-and-insert merge on stable id.

Related pipelines

Early access

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