DatriseAI-first ETL

GitHub Webhook to Apache Superset Integration

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

How Datrise loads GitHub Webhook into Apache Superset

Datrise syncs GitHub Webhook's push events, pull requests, issues, releases, commits, and workflow runs into Apache Superset as governed SQL tables Superset queries directly. Flexible or custom fields land in flattened columns for the explore UI, and timestamps such as created, updated, and status changes are typed as temporal columns for time-series charts.

Sync is incremental: Datrise uses incremental refresh of the queried tables, so re-runs update only what changed. Date-partitioned tables to keep dashboards responsive. Superset charts run live SQL, so Datrise lands query-friendly, indexed tables rather than wide raw payloads.

Ideal for open-source dashboards over your own database.

Endpoints

GitHub Webhook: Repository event stream delivered by webhook.

Apache Superset: Open-source BI for SQL exploration, charts, and dashboard publishing.

How GitHub Webhook entities map to Apache Superset

GitHub Webhook entityApache Superset objectNotes
push eventsgithub_webhook_push_eventstemporal columns for time-series charts 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 Apache Superset?

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

How does the GitHub Webhook to Apache Superset sync stay up to date?

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

Related pipelines

Early access

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