Summary
lakeFS is a data version control system that applies Git-like operations (branching, committing, merging, reverting) to data lakes. It aims to improve efficiency, collaboration, and reproducibility for machine learning and data engineering projects by providing isolated development environments, data lineage, and the ability to roll back data changes.
Features8/15
See allMust Have
2 of 5
Cloud Storage Integration
Privacy Controls
AI File Chat
Semantic Search
Automated Sorting Rules
Other
6 of 10
Manual Approval Workflow
Local File Access
Multi-User Collaboration
Enterprise SSO & Compliance
Centralized Team Billing
Data Encryption & Security
Feedback-Driven Refinement
Demo Mode
Usage Credits & Quotas
Advanced AI Model
PricingTiered
See allOpen Source
- Format-Agnostic Data Version Control
- Cloud-Agnostic
- Zero Clone copy for isolated environment (via branches)
- Atomic Data Promotion (via merges)
- Data Stays in One Place
- Configurable Garbage Collection
- Data CI/CD Using lakeFS Hooks
- Integrates with Your Data Stack
- Role-Based Access Control (RBAC)
- Run locally
Enterprise
- Format-Agnostic Data Version Control
- Cloud-Agnostic
- Zero Clone copy for isolated environment (via branches)
- Atomic Data Promotion (via merges)
- Data Stays in One Place
- Configurable Garbage Collection
- Data CI/CD Using lakeFS Hooks
- Integrates with Your Data Stack
- Role-Based Access Control (RBAC)
- Single Sign On (SSO)
- SCIM Support
- IAM Roles
- Mount Capability
- Audit Logs
- Transactional Mirroring
- Simplified Garbage Collection (Managed or Standalone)
- SOC2
- Support SLA
Rationale
lakeFS is a data version control system for data lakes, offering Git-like operations for data. While it provides versioning, branching, and merging for data, which can help with reproducibility and collaboration in ML/AI workflows, it does not explicitly offer AI-powered file organization features like AI file chat, semantic search, or automated sorting rules for general file management. Its focus is on data versioning for large datasets, particularly for ML and data engineering, rather than intelligent file organization for individuals or small teams.
