DatriseAI-first ETL

Pivotal Tracker Looker

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

How Datrise loads Pivotal Tracker into Looker

Datrise syncs Pivotal Tracker's records, events, and configuration objects into Looker as governed warehouse tables with LookML-ready naming. Flexible or custom fields land in flattened columns (nested fields expanded for modeling), and timestamps such as created, updated, and status changes are typed as date/time dimension columns.

Sync is incremental: Datrise uses incremental refresh of the underlying warehouse tables Looker explores, so re-runs update only what changed. Date-partitioned fact tables for PDT performance. Looker models live in LookML on top of SQL, so Datrise lands clean, stable column names rather than churn that would break your views.

Ideal for governed, version-controlled BI on a warehouse.

Endpoints

Pivotal Tracker: SaaS or API data source for analytics and warehouse sync.

Looker: Google Cloud BI with LookML semantic models and governed dashboards.

How Pivotal Tracker entities map to Looker

Pivotal Tracker entityLooker objectNotes
recordspivotal_tracker_recordsid PK · custom fields → flattened columns (nested fields expanded for modeling)
eventspivotal_tracker_eventsdate/time dimension columns events
configuration objectspivotal_tracker_configuration_objectsid PK · linked to pivotal_tracker_records

FAQ

How does Datrise handle Pivotal Tracker's custom fields in Looker?

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

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

It runs incrementally — Datrise uses incremental refresh of the underlying warehouse tables Looker explores.

Related pipelines

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