DatriseAI-first ETL

GitHub Looker Studio

AI-first ETL from GitHub into Looker Studio. Governed entities, incremental sync, typed landing tables.

How Datrise loads GitHub into Looker Studio

Datrise syncs GitHub's repositories, issues, pull requests, commits, and workflow runs into Looker Studio as warehouse tables Looker Studio connects to. Flexible or custom fields land in flattened columns for chart fields, and timestamps such as created, updated, and status changes are typed as date dimension columns.

Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned tables to keep extract refresh fast. Looker Studio performs best on pre-aggregated tables, so Datrise lands tidy, report-shaped tables rather than raw API payloads.

Ideal for free, shareable dashboards on Google data sources.

Endpoints

GitHub: Developer platform for repos, issues, and delivery workflows.

Looker Studio: Google self-service dashboards and reporting (formerly Data Studio).

How GitHub entities map to Looker Studio

GitHub entityLooker Studio objectNotes
repositoriesgithub_repositoriesid PK · custom fields → flattened columns for chart fields
issuesgithub_issuesid PK · linked to github_repositories
pull requestsgithub_pull_requestsid PK · linked to github_repositories
commitsgithub_commitsid PK · linked to github_repositories

FAQ

How does Datrise handle GitHub's custom fields in Looker Studio?

Flexible values are stored as flattened columns for chart fields, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Looker Studio types.

How does the GitHub to Looker Studio sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the connected tables.

Related pipelines

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