DatriseAI-first ETL

Pivotal Tracker Mode

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

How Datrise loads Pivotal Tracker into Mode

Datrise syncs Pivotal Tracker's records, events, and configuration objects into Mode as warehouse tables Mode queries with SQL. Flexible or custom fields land in flattened columns for SQL and notebooks, and timestamps such as created, updated, and status changes are typed as temporal columns.

Sync is incremental: Datrise uses incremental refresh of the queried tables, so re-runs update only what changed. Date-partitioned facts for report queries. Mode runs analyst-written SQL, so Datrise lands stable, documented tables that won't break saved reports.

Ideal for SQL-first analysis with Python and R notebooks.

Endpoints

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

Mode: Collaborative analytics workspace for SQL, Python, and shared reports.

How Pivotal Tracker entities map to Mode

Pivotal Tracker entityMode objectNotes
recordspivotal_tracker_recordsid PK · custom fields → flattened columns for SQL and notebooks
eventspivotal_tracker_eventstemporal columns events
configuration objectspivotal_tracker_configuration_objectsid PK · linked to pivotal_tracker_records

FAQ

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

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

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

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

Related pipelines

Early access

Connect Pivotal Tracker to Mode the easy way

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