DatriseAI-first ETL

Heroku to Qlik Integration

AI-first ETL from Heroku into Qlik. Governed entities, incremental sync, typed landing tables.

How Datrise loads Heroku into Qlik

Datrise syncs Heroku's apps, dynos, releases, add-ons, config vars, and build logs into Qlik as tables loaded into Qlik's associative engine (often via QVD). Flexible or custom fields land in flattened columns for the data model, and timestamps such as created, updated, and status changes are typed as date/time fields.

Sync is incremental: Datrise uses incremental QVD loads merged on stable id, so re-runs update only what changed. QVD files per entity and load date. Qlik's associative model joins on identically named fields, so Datrise standardizes key names so associations link correctly.

Ideal for associative, in-memory exploration in Qlik Sense.

Endpoints

Heroku: Application platform with dynos and add-ons.

Qlik: Associative analytics with Qlik Sense apps and governed data models.

How Heroku entities map to Qlik

Heroku entityQlik objectNotes
appsheroku_appsid PK · custom fields → flattened columns for the data model
dynosheroku_dynosid PK · linked to heroku_apps
releasesheroku_releasesid PK · linked to heroku_apps
add-onsheroku_add_onsid PK · linked to heroku_apps

FAQ

How does Datrise handle Heroku's custom fields in Qlik?

Flexible values are stored as flattened columns for the data model, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Qlik types.

How does the Heroku to Qlik sync stay up to date?

It runs incrementally — Datrise uses incremental QVD loads merged on stable id.

Related pipelines

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