Heroku to Amazon Redshift Integration
AI-first ETL from Heroku into Amazon Redshift. Governed entities, incremental sync, typed landing tables.
How Datrise loads Heroku into Amazon Redshift
Datrise syncs Heroku's apps, dynos, releases, add-ons, config vars, and build logs into Amazon Redshift as a typed table per source entity. Flexible or custom fields land in SUPER columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMPTZ.
Sync is incremental: Datrise uses COPY from staged files, then a delete-and-insert merge on stable id, so re-runs update only what changed. A DISTKEY on the join id and a SORTKEY on the load timestamp. Redshift performance hinges on dist/sort keys, so Datrise picks them from your entity ids and sync timestamps rather than defaulting to EVEN distribution.
Ideal for AWS-native warehouses already using the Redshift ecosystem.
Endpoints
Heroku: Application platform with dynos and add-ons.
Amazon Redshift: AWS petabyte-scale warehouse with Spectrum.
How Heroku entities map to Amazon Redshift
| Heroku entity | Amazon Redshift object | Notes |
|---|---|---|
| apps | heroku_apps | id PK · custom fields → SUPER 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 Amazon Redshift?
Flexible values are stored as SUPER columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon Redshift types.
How does the Heroku to Amazon Redshift sync stay up to date?
It runs incrementally — Datrise uses COPY from staged files, then a delete-and-insert merge on stable id.
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Early access
Connect Heroku to Amazon Redshift the easy way
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