GitHub Webhook to DuckDB Integration

How Datrise loads GitHub Webhook into DuckDB

Datrise syncs GitHub Webhook's push events, pull requests, issues, releases, commits, and workflow runs into DuckDB as a typed table per source entity in a DuckDB file. Flexible or custom fields land in JSON or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP WITH TIME ZONE.

Sync is incremental: Datrise uses rewrites changed entities into the local database (or Parquet) on each run, so re-runs update only what changed. Hive-partitioned Parquet by load date when exporting. DuckDB is single-writer and embedded, so Datrise produces a consistent file snapshot rather than concurrent streaming writes.

Ideal for local and notebook analytics without standing up a server.

Endpoints

GitHub Webhook: Repository event stream delivered by webhook.

DuckDB: In-process analytics database for fast local OLAP.

How GitHub Webhook entities map to DuckDB

GitHub Webhook entity	DuckDB object	Notes
push events	`github_webhook_push_events`	TIMESTAMP WITH TIME ZONE events
pull requests	`github_webhook_pull_requests`	id PK · linked to github_webhook_push_events
issues	`github_webhook_issues`	id PK · linked to github_webhook_push_events
releases	`github_webhook_releases`	id PK · linked to github_webhook_push_events

FAQ

How does Datrise handle GitHub Webhook's custom fields in DuckDB?

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

How does the GitHub Webhook to DuckDB sync stay up to date?

It runs incrementally — Datrise uses rewrites changed entities into the local database (or Parquet) on each run.

Related pipelines

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