nessie
dvc
nessie | dvc | |
---|---|---|
13 | 109 | |
834 | 13,139 | |
3.6% | 0.6% | |
9.9 | 9.6 | |
4 days ago | 3 days ago | |
Java | 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.
nessie
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A deep dive into the concept and world of Apache Iceberg Catalogs
Nessie is an innovative open-source catalog that extends beyond the traditional catalog capabilities in the Apache Iceberg ecosystem, introducing git-like features to data management. This catalog not only tracks table metadata but also allows users to capture commits at a holistic level, enabling advanced operations such as multi-table transactions, rollbacks, branching, and tagging. These features provide a new layer of flexibility and control over data changes, resembling version control systems in software development.
- FLaNK Stack Weekly 22 January 2024
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Why is Hive Metastore everywhere? (Especially Iceberg)
Try Nessie https://github.com/projectnessie/nessie - it recently got trino support as well ..
- What are the main things I need to know to be hired as a Java developer?
- Is learning and mastering Spring & Spring boot worth it in 2023 ?
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Which lakehouse table format do you expect your organization will be using by the end of 2023?
Project Nessie (https://projectnessie.org/) will be the catalog that eventually decouples Iceberg from Hive. At that point, I think it will be a no brainer to go Iceberg over Delta.
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5 Reasons Your Data Lakehouse should Embrace Dremio Cloud
The Dremio Sonar query engine can query your data where it exists whether it's AWS Glue, S3, Nessie Catalogs, MySQL, Postgres, RedShift and an ever growing list of sources.
- Project Nessie: Transactional Catalog for Data Lakes with Git-Like Semantics
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Introduction to The World of Data - (OLTP, OLAP, Data Warehouses, Data Lakes and more)
We will also need a catalog to track all of these tables, with the open source Project Nessie we can do just that, and also get great versioning features similar to using Git when developing applications allowing data engineers to practice "data as code" and "write-audit-publish" patterns on their data.
- DoltLab v0.2.0
dvc
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My Favorite DevTools to Build AI/ML Applications!
Collaboration and version control are crucial in AI/ML development projects due to the iterative nature of model development and the need for reproducibility. GitHub is the leading platform for source code management, allowing teams to collaborate on code, track issues, and manage project milestones. DVC (Data Version Control) complements Git by handling large data files, data sets, and machine learning models that Git can't manage effectively, enabling version control for the data and model files used in AI projects.
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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).
https://dvc.org/
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.
What are some alternatives?
git-bug - Distributed, offline-first bug tracker embedded in git, with bridges
MLflow - Open source platform for the machine learning lifecycle
hiveberg - Demonstration of a Hive Input Format for Iceberg
lakeFS - lakeFS - Data version control for your data lake | Git for data
dremio-oss - Dremio - the missing link in modern data
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]
noms - The versioned, forkable, syncable database
delta - An open-source storage framework that enables building a Lakehouse architecture with compute engines including Spark, PrestoDB, Flink, Trino, and Hive and APIs
dolt - Dolt – Git for Data
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
Flyway - Flyway by Redgate • Database Migrations Made Easy.
aim - Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.