DatriseAI-first ETL

Pivotal Tracker to Redash Integration

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

How Datrise loads Pivotal Tracker into Redash

Datrise syncs Pivotal Tracker's stories, projects, iterations, epics, tasks, and story transitions into Redash as SQL tables Redash queries and visualizes. Flexible or custom fields land in flattened columns for query results, and timestamps such as created, updated, and status changes are typed as temporal columns.

Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned facts for scheduled queries. Redash caches query results on a schedule, so Datrise keeps tables incrementally fresh so cached dashboards reflect reality.

Ideal for lightweight, query-driven dashboards.

Endpoints

Pivotal Tracker: Agile story tracking and iteration planning.

Redash: Open-source SQL client for queries, visualizations, and dashboards.

How Pivotal Tracker entities map to Redash

Pivotal Tracker entityRedash objectNotes
storiespivotal_tracker_storiesid PK · custom fields → flattened columns for query results
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 Redash?

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

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

It runs incrementally — Datrise uses incremental refresh of the connected tables.

Related pipelines

Early access

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