DatriseAI-first ETL

Urban Airship Tableau

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

How Datrise loads Urban Airship into Tableau

Datrise syncs Urban Airship's records, events, and configuration objects 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

Urban Airship: SaaS or API data source for analytics and warehouse sync.

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

How Urban Airship entities map to Tableau

Urban Airship entityTableau objectNotes
recordsurban_airship_recordsid PK · custom fields → flattened columns for Tableau fields
eventsurban_airship_eventsdate/datetime fields events
configuration objectsurban_airship_configuration_objectsid PK · linked to urban_airship_records

FAQ

How does Datrise handle Urban Airship'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 Urban Airship 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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