Overview
Datachain delivers ETL, analytics, and versioning capabilities for unstructured data, enabling teams to build reproducible and auditable data pipelines. The project combines data management and version control concepts to maintain consistency and traceability across model training, evaluation, and production workflows.
Key Features
- Data versioning: Version control for unstructured datasets with traceability.
- ETL & analytics: Support for document processing, feature extraction, and downstream analytics.
- ML toolchain integration: Easily connects data pipelines to training and evaluation stages.
Use Cases
- Training data management: Maintain reproducible dataset versions during iterative model development.
- Data auditing & compliance: Support audit trails and provenance for datasets used in production.
- Data engineering pipelines: Build standardized preprocessing workflows for embeddings and retrieval.
Technical Details
- Stack: Python-first tooling with integrations to common storage and processing backends.
- Extensibility: Modular design for plugging into various storage, retrieval, and model systems.
- License: Apache-2.0 for enterprise and community adoption.