DatriseAI-first ETL

GitHub Webhook to Google BigQuery Integration

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

How Datrise loads GitHub Webhook into Google BigQuery

Datrise syncs GitHub Webhook's push events, pull requests, issues, releases, commits, and workflow runs into Google BigQuery as a partitioned table per source entity. Flexible or custom fields land in JSON or nested/repeated (STRUCT) columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target, so re-runs update only what changed. Partition by ingestion or event date and cluster by entity id to keep scanned bytes low. BigQuery bills by bytes scanned, so Datrise partitions and clusters every table to keep query costs predictable.

Ideal for Google-stack analytics and ML on serverless infrastructure.

Endpoints

GitHub Webhook: Repository event stream delivered by webhook.

Google BigQuery: Serverless analytics warehouse on GCP.

How GitHub Webhook entities map to Google BigQuery

GitHub Webhook entityGoogle BigQuery objectNotes
push eventsgithub_webhook_push_eventsTIMESTAMP 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 Google BigQuery?

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

How does the GitHub Webhook to Google BigQuery sync stay up to date?

It runs incrementally — Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target.

Related pipelines

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