DatriseAI-first ETL

Pivotal Tracker to Looker Studio Integration

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

How Datrise loads Pivotal Tracker into Looker Studio

Datrise syncs Pivotal Tracker's stories, projects, iterations, epics, tasks, and story transitions into Looker Studio as warehouse tables Looker Studio connects to. Flexible or custom fields land in flattened columns for chart fields, and timestamps such as created, updated, and status changes are typed as date dimension columns.

Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned tables to keep extract refresh fast. Looker Studio performs best on pre-aggregated tables, so Datrise lands tidy, report-shaped tables rather than raw API payloads.

Ideal for free, shareable dashboards on Google data sources.

Endpoints

Pivotal Tracker: Agile story tracking and iteration planning.

Looker Studio: Google self-service dashboards and reporting (formerly Data Studio).

How Pivotal Tracker entities map to Looker Studio

Pivotal Tracker entityLooker Studio objectNotes
storiespivotal_tracker_storiesid PK · custom fields → flattened columns for chart fields
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 Looker Studio?

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

How does the Pivotal Tracker to Looker Studio sync stay up to date?

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

Related pipelines

Early access

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