DatriseAI-first ETL

Pivotal Tracker to PlanetScale Integration

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

How Datrise loads Pivotal Tracker into PlanetScale

Datrise syncs Pivotal Tracker's stories, projects, iterations, epics, tasks, and story transitions into PlanetScale as a typed table per source entity. Flexible or custom fields land in JSON columns, and timestamps such as created, updated, and status changes are typed as DATETIME.

Sync is incremental: Datrise uses a watermark on updated-at, applied with INSERT … ON DUPLICATE KEY UPDATE, so re-runs update only what changed. Vitess sharding by tenant or entity key for very large tables. PlanetScale disallows foreign-key constraints by default, so Datrise models relationships by stable id columns rather than enforced FKs.

Ideal for horizontally scalable MySQL apps on Vitess.

Endpoints

Pivotal Tracker: Agile story tracking and iteration planning.

PlanetScale: Serverless MySQL platform with safe schema workflows.

How Pivotal Tracker entities map to PlanetScale

Pivotal Tracker entityPlanetScale objectNotes
storiespivotal_tracker_storiesid PK · custom fields → JSON columns
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 PlanetScale?

Flexible values are stored as JSON columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native PlanetScale types.

How does the Pivotal Tracker to PlanetScale sync stay up to date?

It runs incrementally — Datrise uses a watermark on updated-at, applied with INSERT … ON DUPLICATE KEY UPDATE.

Related pipelines

Early access

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