DatriseAI-first ETL

Pivotal Tracker Yellowfin

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

How Datrise loads Pivotal Tracker into Yellowfin

Datrise syncs Pivotal Tracker's records, events, and configuration objects into Yellowfin as warehouse tables Yellowfin builds views on. Flexible or custom fields land in flattened columns, and timestamps such as created, updated, and status changes are typed as date/time dimensions.

Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned facts. Yellowfin views reference columns by name, so Datrise lands stable, well-typed columns to keep reports valid.

Ideal for dashboards with automated data storytelling.

Endpoints

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

Yellowfin: BI suite with dashboards, automated insights, and data storytelling.

How Pivotal Tracker entities map to Yellowfin

Pivotal Tracker entityYellowfin objectNotes
recordspivotal_tracker_recordsid PK · custom fields → flattened columns
eventspivotal_tracker_eventsdate/time dimensions events
configuration objectspivotal_tracker_configuration_objectsid PK · linked to pivotal_tracker_records

FAQ

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

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

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

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

Related pipelines

Early access

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