DatriseAI-first ETL

Lokalise to Databricks SQL Warehouse Integration

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

How Datrise loads Lokalise into Databricks SQL Warehouse

Datrise syncs Lokalise's projects, keys, translations, languages, contributors, and translation statuses into Databricks SQL Warehouse as a Delta Lake table per source entity. Flexible or custom fields land in VARIANT or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses a Delta MERGE on stable id, with change history available via time travel, so re-runs update only what changed. Delta partitioning by load date with OPTIMIZE/Z-ORDER on query keys. Datrise writes Unity Catalog–governed Delta tables, so lineage and permissions are managed centrally rather than per-notebook.

Ideal for lakehouse analytics and ML feature tables on Databricks.

Endpoints

Lokalise: Translation management for product strings.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Lokalise entities map to Databricks SQL Warehouse

Lokalise entityDatabricks SQL Warehouse objectNotes
projectslokalise_projectsid PK · custom fields → VARIANT or STRUCT columns
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 Databricks SQL Warehouse?

Flexible values are stored as VARIANT or STRUCT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Databricks SQL Warehouse types.

How does the Lokalise to Databricks SQL Warehouse sync stay up to date?

It runs incrementally — Datrise uses a Delta MERGE on stable id, with change history available via time travel.

Related pipelines

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