lakeFS
dvc
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| lakeFS | dvc | |
|---|---|---|
| 48 | 108 | |
| 4,053 | 13,093 | |
| 2.2% | 1.3% | |
| 9.8 | 9.7 | |
| 7 days ago | 2 days ago | |
| Go | Python | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
lakeFS
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A Step-by-Step Guide to Implementing Data Version Control
# Download the LakeFS binary wget https://github.com/treeverse/lakeFS/releases/latest/download/lakefs # Make the binary executable chmod +x lakefs # Initialize LakeFS with S3 as the storage backend ./lakefs init --backend s3 --s3-gateway-endpoint --s3-region --s3-force-path-style --s3-access-key --s3-secret-key
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Jujutsu: A Git-compatible DVCS that is both simple and powerful
Might want to look at purpose built tools for that such as lakeFS (https://github.com/treeverse/lakeFS/)
* Disclaimer: I'm one of the creators/maintainers of the project.
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Data diffs: Algorithms for explaining what changed in a dataset (2022)
Might want to checkout lakeFS: https://github.com/treeverse/lakeFS
(full disclosure: I'm one of the creators)
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Transactions in Spark / Delta lake?
Take a look at https://github.com/treeverse/lakeFS -
- LakeFS – Version Control for Big Data
- DuckDB <3 LakeFS
- We built an open-source project (3.1K stars on GitHub) for data version control
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How are you incrementally testing your data pipelines as you develop them?
I mean if you're ready to adopt a new framework into your ecosystem this is one of the major usecases for LakeFS.
- Git-for-Data
- LakeFS: Git-like versioning for object stores
dvc
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Why bad scientific code beats code following "best practices"
What you’re describing sounds like DVC (at a higher-ish—80%-solution level).
See pachyderm too.
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First 15 Open Source Advent projects
10. DVC by Iterative | Github | tutorial
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
Platforms such as MLflow monitor the development stages of machine learning models. In parallel, Data Version Control (DVC) brings version control system-like functions to the realm of data sets and models.
- ML Experiments Management with Git
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Git Version Controlled Datasets in S3
I was using DVC (https://dvc.org/) for some time to help solve this but it was getting hard to manage the storage connections and I would run into cache issues a lot, but this solves it using git-lfs itself.
- Ask HN: How do your ML teams version datasets and models?
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Exploring MLOps Tools and Frameworks: Enhancing Machine Learning Operations
DVC (Data Version Control):
- Evaluate and Track Your LLM Experiments: Introducing TruLens for LLMs
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[D] Is there a tool to keep track of my ML experiments?
I have been using DVC and MLflow since then DVC had only data tracking and MLflow only model tracking. I can say both are awesome now and maybe the only factor I would like to mention is that IMO, MLflow is a bit harder to learn while DVC is just a git practically.
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Where do I best store my test data when using github for code?
I use DVC, which works decently well and can be hooked into Git.
What are some alternatives?
delta - An open-source storage framework that enables building a Lakehouse architecture with compute engines including Spark, PrestoDB, Flink, Trino, and Hive and APIs
MLflow - Open source platform for the machine learning lifecycle
git-lfs - Git extension for versioning large files
Activeloop Hub - Data Lake for Deep Learning. Build, manage, query, version, & visualize datasets. Stream data real-time to PyTorch/TensorFlow. https://activeloop.ai [Moved to: https://github.com/activeloopai/deeplake]
Ory Kratos - Next-gen identity server replacing your Auth0, Okta, Firebase with hardened security and PassKeys, SMS, OIDC, Social Sign In, MFA, FIDO, TOTP and OTP, WebAuthn, passwordless and much more. Golang, headless, API-first. Available as a worry-free SaaS with the fairest pricing on the market!
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
duf - Disk Usage/Free Utility - a better 'df' alternative
aim - Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
quilt - Quilt is a data mesh for connecting people with actionable data
git-submodules - Git Submodule alternative with equivalent features, but easier to use and maintain.