DatriseAI-first ETL

Lokalise to Amazon S3 Data Lake Integration

AI-first ETL from Lokalise into Amazon S3 Data Lake. Governed entities, incremental sync, typed landing tables.

How Datrise loads Lokalise into Amazon S3 Data Lake

Datrise syncs Lokalise's projects, keys, translations, languages, contributors, and translation statuses into Amazon S3 Data Lake as columnar Parquet objects partitioned per source entity. Flexible or custom fields land in nested struct/map fields in Parquet, and timestamps such as created, updated, and status changes are typed as ISO-8601 timestamp columns.

Sync is incremental: Datrise uses writes new date partitions and compacts small files on a schedule, so re-runs update only what changed. Hive-style path partitioning (entity/date) for engine-agnostic reads. A lake has no schema enforcement, so Datrise writes a schema manifest alongside the data to keep downstream engines consistent.

Ideal for an open, engine-neutral storage layer for Spark, Athena, Trino, or DuckDB.

Endpoints

Lokalise: Translation management for product strings.

Amazon S3 Data Lake: Object storage landing zone for parquet and snapshots.

How Lokalise entities map to Amazon S3 Data Lake

Lokalise entityAmazon S3 Data Lake objectNotes
projectslokalise_projectsid PK · custom fields → nested struct/map fields in Parquet
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 S3 Data Lake?

Flexible values are stored as nested struct/map fields in Parquet, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon S3 Data Lake types.

How does the Lokalise to Amazon S3 Data Lake sync stay up to date?

It runs incrementally — Datrise uses writes new date partitions and compacts small files on a schedule.

Related pipelines

Early access

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