DatriseAI-first ETL

Github Webhook DuckDB

AI-first ETL from Github Webhook into DuckDB. Governed entities, incremental sync, typed landing tables.

How Datrise loads Github Webhook into DuckDB

Datrise syncs Github Webhook's records, events, and configuration objects 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: SaaS or API data source for analytics and warehouse sync.

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

How Github Webhook entities map to DuckDB

Github Webhook entityDuckDB objectNotes
recordsgithub_webhook_recordsid PK · custom fields → JSON or STRUCT columns
eventsgithub_webhook_eventsTIMESTAMP WITH TIME ZONE events
configuration objectsgithub_webhook_configuration_objectsid PK · linked to github_webhook_records

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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