datlr

Datlr is a data management platform designed to help teams store, version, share, and analyze large datasets. It emphasizes reproducibility and collaboration, offering repository-like controls for data alongside traditional file storage, making it suitable for research, engineering, and analytics workflows.

Core features include dataset versioning and lineage, metadata management, and a searchable catalog. Users can import common formats (CSV, JSON, Parquet), link notebooks and pipelines, and access data via REST or GraphQL APIs. It supports authentication, access controls, and audit trails.

Datlr operates as a distributed service with a core data store, a metadata catalog, and microservices for ingestion, search, and collaboration. It can store data on cloud backends such as AWS S3 or Google Cloud Storage or on local storage, with encryption at rest.

Typical use cases include scientific research projects, data science experiments, and teams requiring governance over data assets. Datlr provides governance features such as role-based access, retention policies, and lineage tracing to show data origins and transformations throughout analyses.

Origin and reception: Datlr emerged from an open-source community project in the 2010s and matured into both cloud-hosted and self-hosted deployments. While praised for reproducibility and collaboration, organizations may need dedicated technical resources to deploy and manage large-scale datasets.