DatriseAI-first ETL

Smaily to Tableau Integration

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

How Datrise loads Smaily into Tableau

Datrise syncs Smaily's subscribers, campaigns, lists, autoresponders, email events, and segments 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

Smaily: Email and SMS campaigns with subscriber segments.

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

How Smaily entities map to Tableau

Smaily entityTableau objectNotes
subscriberssmaily_subscribersid PK · custom fields → flattened columns for Tableau fields
campaignssmaily_campaignsid PK · linked to smaily_subscribers
listssmaily_listsid PK · linked to smaily_subscribers
autoresponderssmaily_autorespondersid PK · linked to smaily_subscribers

FAQ

How does Datrise handle Smaily'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 Smaily 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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