DatriseAI-first ETL

Metabase to Tableau Integration

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

How Datrise loads Metabase into Tableau

Datrise syncs Metabase's questions, dashboards, collections, cards, users, and query executions 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

Metabase: Self-service BI over existing databases.

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

How Metabase entities map to Tableau

Metabase entityTableau objectNotes
questionsmetabase_questionsid PK · custom fields → flattened columns for Tableau fields
dashboardsmetabase_dashboardsid PK · linked to metabase_questions
collectionsmetabase_collectionsid PK · linked to metabase_questions
cardsmetabase_cardsid PK · linked to metabase_questions

FAQ

How does Datrise handle Metabase'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 Metabase 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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