DatriseAI-first ETL

GitLab Amazon S3 Data Lake

AI-first ETL from GitLab into Amazon S3 Data Lake. Governed entities, incremental sync, typed landing tables.

How Datrise loads GitLab into Amazon S3 Data Lake

Datrise syncs GitLab's projects, merge requests, pipelines, issues, and deployment events into Amazon S3 Data Lake as columnar Parquet objects partitioned per source entity. Flexible or custom fields land in nested struct/map fields in Parquet, and timestamps such as created, updated, and status changes are typed as ISO-8601 timestamp columns.

Sync is incremental: Datrise uses writes new date partitions and compacts small files on a schedule, so re-runs update only what changed. Hive-style path partitioning (entity/date) for engine-agnostic reads. A lake has no schema enforcement, so Datrise writes a schema manifest alongside the data to keep downstream engines consistent.

Ideal for an open, engine-neutral storage layer for Spark, Athena, Trino, or DuckDB.

Endpoints

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

Amazon S3 Data Lake: Object storage landing zone for parquet and snapshots.

How GitLab entities map to Amazon S3 Data Lake

GitLab entityAmazon S3 Data Lake objectNotes
projectsgitlab_projectsid PK · custom fields → nested struct/map fields in Parquet
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 S3 Data Lake?

Flexible values are stored as nested struct/map fields in Parquet, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon S3 Data Lake types.

How does the GitLab to Amazon S3 Data Lake sync stay up to date?

It runs incrementally — Datrise uses writes new date partitions and compacts small files on a schedule.

Related pipelines

Early access

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