DatriseAI-first ETL

Vitally to Tableau Integration

AI-first ETL from Vitally into Tableau. Governed entities, incremental sync, typed landing tables.

How Datrise loads Vitally into Tableau

Datrise syncs Vitally's accounts, users, health scores, tasks, playbooks, and product usage into Tableau as warehouse tables or a refreshed .hyper extract. Flexible or custom fields land in flattened columns for Tableau fields, and timestamps such as created, updated, and status changes are typed as date/datetime fields.

Sync is incremental: Datrise uses incremental refresh of the tables behind a live connection or extract, so re-runs update only what changed. Date-partitioned facts to keep extract refresh quick. Tableau .hyper extracts snapshot data, so Datrise keeps the source tables incremental and lets you choose live vs extract.

Ideal for visual analytics and dashboards in Tableau.

Endpoints

Vitally: Customer success health and account analytics.

Tableau: Salesforce analytics platform for interactive dashboards and visual exploration.

How Vitally entities map to Tableau

Vitally entityTableau objectNotes
accountsvitally_accountsid PK · custom fields → flattened columns for Tableau fields
usersvitally_usersid PK · linked to vitally_accounts
health scoresvitally_health_scoresid PK · linked to vitally_accounts
tasksvitally_tasksid PK · linked to vitally_accounts

FAQ

How does Datrise handle Vitally's custom fields in Tableau?

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

How does the Vitally to Tableau sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the tables behind a live connection or extract.

Related pipelines

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