DatriseAI-first ETL

Pivotal Tracker to Qlik Integration

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

How Datrise loads Pivotal Tracker into Qlik

Datrise syncs Pivotal Tracker's stories, projects, iterations, epics, tasks, and story transitions 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

Pivotal Tracker: Agile story tracking and iteration planning.

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

How Pivotal Tracker entities map to Qlik

Pivotal Tracker entityQlik objectNotes
storiespivotal_tracker_storiesid PK · custom fields → flattened columns for the data model
projectspivotal_tracker_projectsid PK · linked to pivotal_tracker_stories
iterationspivotal_tracker_iterationsid PK · linked to pivotal_tracker_stories
epicspivotal_tracker_epicsid PK · linked to pivotal_tracker_stories

FAQ

How does Datrise handle Pivotal Tracker'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 Pivotal Tracker to Qlik sync stay up to date?

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

Related pipelines

Early access

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