DatriseAI-first ETL

Pivotal Tracker Domo

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

How Datrise loads Pivotal Tracker into Domo

Datrise syncs Pivotal Tracker's records, events, and configuration objects into Domo as datasets in Domo's cloud store via connector. Flexible or custom fields land in flattened columns for Magic ETL, and timestamps such as created, updated, and status changes are typed as date/time columns.

Sync is incremental: Datrise uses partitioned dataset updates rather than full replaces, so re-runs update only what changed. Domo dataset partitions keyed on load date. Domo stores its own copy of data, so Datrise sends incremental partitions to avoid re-uploading whole datasets.

Ideal for all-in-one cloud BI with built-in ETL.

Endpoints

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

Domo: Cloud BI platform combining data integration and executive dashboards.

How Pivotal Tracker entities map to Domo

Pivotal Tracker entityDomo objectNotes
recordspivotal_tracker_recordsid PK · custom fields → flattened columns for Magic ETL
eventspivotal_tracker_eventsdate/time columns events
configuration objectspivotal_tracker_configuration_objectsid PK · linked to pivotal_tracker_records

FAQ

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

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

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

It runs incrementally — Datrise uses partitioned dataset updates rather than full replaces.

Related pipelines

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