lakeFS is a data version control platform that manages the data lifecycle, provenance, and unified access for AI and data teams.
lakeFS is a data version control system that brings Git-like capabilities to data lakes and object storage. It enables data teams to manage data as code, providing features such as branching, merging, and reverting for data. This allows for experimentation, reproducibility, and data quality enforcement. lakeFS supports various storage solutions like Amazon S3, Google Cloud Storage, and Azure Blob Storage. It integrates with compute engines such as Spark, Trino, and Databricks, and is format-agnostic, working with Parquet, CSV, Avro, and more. lakeFS is designed for data engineers, data scientists, and MLOps practitioners who need to manage large datasets, ensure data quality, and streamline data workflows for AI and machine learning projects.
lakeFS is a data version control system that brings Git-like capabilities to data lakes and object storage.
Explore all tools that specialize in create data branches for experimentation. This domain focus ensures lakeFS delivers optimized results for this specific requirement.
Explore all tools that specialize in enforce data quality policies through pre-commit hooks. This domain focus ensures lakeFS delivers optimized results for this specific requirement.
Explore all tools that specialize in connect to object storage (s3, gcs, azure). This domain focus ensures lakeFS delivers optimized results for this specific requirement.
Creates isolated copies of data for experimentation and development without affecting the production data.
Tracks changes to data over time, allowing users to revert to previous versions if needed.
Combines changes from different data branches into a single, consistent dataset.
Tracks the origin and transformation of data, providing visibility into the entire data lifecycle.
Manages user permissions and access to data resources.
Install lakeFS using Docker or Kubernetes.
Configure lakeFS to connect to your object storage (e.g., Amazon S3).
Create a repository in lakeFS to store your data.
Import your existing data into the lakeFS repository.
Create a branch to isolate your changes.
Modify your data and commit the changes to your branch.
Merge your branch back into the main branch to apply your changes.
All Set
Ready to go
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"Users praise lakeFS for its ability to streamline data science and MLOps workflows, improve robustness and flexibility of data systems, and reduce testing time."
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