DatriseAI-first ETL

GitHub Webhook to Looker Studio Integration

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

How Datrise loads GitHub Webhook into Looker Studio

Datrise syncs GitHub Webhook's push events, pull requests, issues, releases, 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 Webhook: Repository event stream delivered by webhook.

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

How GitHub Webhook entities map to Looker Studio

GitHub Webhook entityLooker Studio objectNotes
push eventsgithub_webhook_push_eventsdate dimension columns events
pull requestsgithub_webhook_pull_requestsid PK · linked to github_webhook_push_events
issuesgithub_webhook_issuesid PK · linked to github_webhook_push_events
releasesgithub_webhook_releasesid PK · linked to github_webhook_push_events

FAQ

How does Datrise handle GitHub Webhook'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 Webhook 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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