DatriseAI-first ETL

Pivotal Tracker to Mode Integration

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 stories, projects, iterations, epics, tasks, and story transitions 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: Agile story tracking and iteration planning.

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

How Pivotal Tracker entities map to Mode

Pivotal Tracker entityMode objectNotes
storiespivotal_tracker_storiesid PK · custom fields → flattened columns for SQL and notebooks
projectspivotal_tracker_projectsid PK · linked to pivotal_tracker_stories
iterationspivotal_tracker_iterationsid PK · linked to pivotal_tracker_stories
epicspivotal_tracker_epicsid PK · linked to pivotal_tracker_stories

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

Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.