DatriseAI-first ETL

GitLab Databricks SQL Warehouse

AI-first ETL from GitLab into Databricks SQL Warehouse. Governed entities, incremental sync, typed landing tables.

How Datrise loads GitLab into Databricks SQL Warehouse

Datrise syncs GitLab's projects, merge requests, pipelines, issues, and deployment events into Databricks SQL Warehouse as a Delta Lake table per source entity. Flexible or custom fields land in VARIANT or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses a Delta MERGE on stable id, with change history available via time travel, so re-runs update only what changed. Delta partitioning by load date with OPTIMIZE/Z-ORDER on query keys. Datrise writes Unity Catalog–governed Delta tables, so lineage and permissions are managed centrally rather than per-notebook.

Ideal for lakehouse analytics and ML feature tables on Databricks.

Endpoints

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

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How GitLab entities map to Databricks SQL Warehouse

GitLab entityDatabricks SQL Warehouse objectNotes
projectsgitlab_projectsid PK · custom fields → VARIANT or 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 Databricks SQL Warehouse?

Flexible values are stored as VARIANT or STRUCT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Databricks SQL Warehouse types.

How does the GitLab to Databricks SQL Warehouse sync stay up to date?

It runs incrementally — Datrise uses a Delta MERGE on stable id, with change history available via time travel.

Related pipelines

Early access

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