DatriseAI-first ETL

Lokalise to Amazon QuickSight Integration

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

How Datrise loads Lokalise into Amazon QuickSight

Datrise syncs Lokalise's projects, keys, translations, languages, contributors, and translation statuses into Amazon QuickSight as warehouse tables or a SPICE-loaded dataset. Flexible or custom fields land in flattened columns for analyses, and timestamps such as created, updated, and status changes are typed as date/time fields.

Sync is incremental: Datrise uses incremental refresh of the tables behind SPICE or direct query, so re-runs update only what changed. Date-partitioned facts to bound SPICE refresh. QuickSight SPICE is an in-memory copy, so Datrise keeps the backing tables incremental so refreshes stay cheap.

Ideal for AWS-native dashboards with pay-per-session pricing.

Endpoints

Lokalise: Translation management for product strings.

Amazon QuickSight: AWS serverless BI with SPICE and embedded analytics.

How Lokalise entities map to Amazon QuickSight

Lokalise entityAmazon QuickSight objectNotes
projectslokalise_projectsid PK · custom fields → flattened columns for analyses
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 Amazon QuickSight?

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

How does the Lokalise to Amazon QuickSight sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the tables behind SPICE or direct query.

Related pipelines

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