lakeFS Acquires DVC, Uniting Data Version Control Pioneers to Accelerate AI-Ready Data
How lakeFS Open Source Compares with lakeFS Enterprise
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- Implement Data Version Control Best Practices
- Maintain Security and Governance
- Improve AI Data Quality
- Avoid the Data Swamp
Data Version Control Essentials
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Create repositories Define the datasets you wish to version control together |
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Commit Save a snapshot of the repository as a version you can access using the commit ID |
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Branch Create a branch of a repository to get an isolated version of it |
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Merge Integrate your changes to the repository safely |
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Enhance Security and Governance
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Single Sign-On (SSO) Connect with your authenticator of choice and seamlessly manage access |
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Role-based access control (RBAC) Effortlessly govern user permissions from one central location |
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IAM Role support Authentication using AWS IAM roles, removing the need to maintain static credentials for lakeFS Enterprise users running on AWS |
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SCIM support Integrate with your existing SCIM provider to ensure user identities
are always synchronized |
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Audit logging Satisfy audit requirements and quickly isolate unexpected changes |
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Short lived tokens Get temporary, secure logins using an Identity Provider, simplifying user access and enhancing security |
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Advanced security Connect via PrivateLink, restrict IP addresses, and more |
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Enterprise support and training Receive premium support |
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AI/ML Workflows
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Work with data locally |
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Integration with your Git/Github/Gitlab |
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lakeFS Mount Allows users to virtually mount a remote lakeFS repository onto a local directory |
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Advanced metadata search Granular search API to filter and query versioned objects based on attached metadata |
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Smoothly Integrate Into Your Workflows
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User management Define multiple users in the lakeFS system |
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lakeFS for Databricks Support for Unity Catalog and any Databricks offerings that rely on it |
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Command Line Interface (CLI)Develop using your terminal or notebook of choice |
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Create your own lakeFS client Use swagger to create a lakefs client in any programming language |
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Rich Python Client Use a lakeFS Python Client that suits your data environment |
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Hadoop Client Available when using Hadoop native compute, such as Spark |
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Deep integration with Orchestration tools Run data pipelines in isolation, from your orchestration tool of choice |
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GUI If you use Git, you’ll feel right at home. View and list your data, see diff results, and perform git-like operations on your data |
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S3 Gateway Use lakeFS as your storage layer and perform both storage operations such as put, get and list, and git-like operations directly through lakeFS |
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Reduce Maintenance Overhead
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Cloud-native No deployment, installation, maintenance and scaling overhead |
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Elastic scalingScale to support development, testing, and production workloads while optimizing data platform costs |
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Managed data retention policies management lakeFS GC is managed for you, allowing retention policies with your required business logic |
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Private-LinkEnsure network security by only allowing access to your lakeFS Cloud installation from your cloud accounts |
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Automatically keep your team on the latest version of lakeFS |
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Transactional Mirroring Replicate lakeFS repositories into consistent read-only copies in remote locations |
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SLA guarantees 99.9% uptime guarantee |
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Follow Engineering Best Practices In Data
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lakeFS Hooks Implement engineering best practices by creating and executing webhooks to perform pre/post commit/merge actions |
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Protected Branches Use branch protection to ensure data changes in main are approved and monitored |
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Advanced Merge strategies Select a merge logic from the list of predefined merge strategies, or create your own |
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Integration with data observability tools Audit data quality when implementing Write-Audit-Publish patterns, using your data quality tool of choice |
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Integration with data compute tools Define the dataset you need to operate on using lakeFS, and use any compute tool directly from your notebook to perform the operation |
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Avoid the Data Swamp
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Import A zero copy operation to load data into your repository |
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Upload data to lakeFSCopy data to the bucket managed by lakeFS using a number of tools |
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Control your storage costs Use zero-copy operations rather than duplicating data |
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Data retention policies management Define and execute data retention policies easily based on business logic |
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Rich object and commit metadata Save relevant metadata with object or commit and version them |
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Version together. Experiment freely
lakeFS Enterprise provides the only data version control system that can
integrate across your entire data architecture, enabling teams to collaborate while maintaining data quality, security and governance
ML Data Reproducibility
Faster Time To Market
Increased Data Quality
lakeFS enabled us to reproduce ML experiments, increase productivity of the data teams, and adhere to FDA compliance requirements 
With lakeFS, we have streamlined data science and MLOps workflows, adapted data access controls for different data teams, accelerated productivity and reduced time-to-insights for ML engineering projects 
lakeFS gives you the ability to branch, roll back, and curate your data while at the same time serving as the source of truth. It just works very well and very fast 
