DatriseAI-first ETL

Lokalise to Tableau Integration

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

How Datrise loads Lokalise into Tableau

Datrise syncs Lokalise's projects, keys, translations, languages, contributors, and translation statuses 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

Lokalise: Translation management for product strings.

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

How Lokalise entities map to Tableau

Lokalise entityTableau objectNotes
projectslokalise_projectsid PK · custom fields → flattened columns for Tableau fields
keyslokalise_keysid PK · linked to lokalise_projects
translationslokalise_translationsid PK · linked to lokalise_projects
languageslokalise_languagesid PK · linked to lokalise_projects

FAQ

How does Datrise handle Lokalise'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 Lokalise 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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