Pivotal Tracker → Looker Studio
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 records, events, and configuration objects 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: SaaS or API data source for analytics and warehouse sync.
Looker Studio: Google self-service dashboards and reporting (formerly Data Studio).
How Pivotal Tracker entities map to Looker Studio
| Pivotal Tracker entity | Looker Studio object | Notes |
|---|---|---|
| records | pivotal_tracker_records | id PK · custom fields → flattened columns for chart fields |
| events | pivotal_tracker_events | date dimension columns events |
| configuration objects | pivotal_tracker_configuration_objects | id PK · linked to pivotal_tracker_records |
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
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Early access
Connect Pivotal Tracker to Looker Studio the easy way
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