Heroku to Google BigQuery Integration

How Datrise loads Heroku into Google BigQuery

Datrise syncs Heroku's apps, dynos, releases, add-ons, config vars, and build logs 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

Heroku: Application platform with dynos and add-ons.

Google BigQuery: Serverless analytics warehouse on GCP.

How Heroku entities map to Google BigQuery

Heroku entity	Google BigQuery object	Notes
apps	`heroku_apps`	id PK · custom fields → JSON or nested/repeated (STRUCT) columns
dynos	`heroku_dynos`	id PK · linked to heroku_apps
releases	`heroku_releases`	id PK · linked to heroku_apps
add-ons	`heroku_add_ons`	id PK · linked to heroku_apps

FAQ

How does Datrise handle Heroku'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 Heroku 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

Early access

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