temporian
pachyderm
temporian | pachyderm | |
---|---|---|
12 | 8 | |
629 | 6,078 | |
3.2% | 0.2% | |
9.8 | 9.8 | |
5 days ago | 5 days ago | |
Python | Go | |
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.
temporian
pachyderm
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Open Source Advent Fun Wraps Up!
20. Pachyderm | Github | tutorial
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
Pachyderm specializes in creating compliance-focused pipelines that integrate with enterprise-level storage solutions.
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Show HN: We scaled Git to support 1 TB repos
There are a couple of other contenders in this space. DVC (https://dvc.org/) seems most similar.
If you're interested in something you can self-host... I work on Pachyderm (https://github.com/pachyderm/pachyderm), which doesn't have a Git-like interface, but also implements data versioning. Our approach de-duplicates between files (even very small files), and our storage algorithm doesn't create objects proportional to O(n) directory nesting depth as Xet appears to. (Xet is very much like Git in that respect.)
The data versioning system enables us to run pipelines based on changes to your data; the pipelines declare what files they read, and that allows us to schedule processing jobs that only reprocess new or changed data, while still giving you a full view of what "would" have happened if all the data had been reprocessed. This, to me, is the key advantage of data versioning; you can save hundreds of thousands of dollars on compute. Being able to undo an oopsie is just icing on the cake.
Xet's system for mounting a remote repo as a filesystem is a good idea. We do that too :)
- pachyderm: Data-Centric Pipelines and Data Versioning
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Awesome list of VCs investing in commercial open-source startups
Pachyderm - License prevents competition.
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Airflow's Problem
I was at Airbnb when we open-sourced Airflow, it was a great solution to the problems we had at the time. It's amazing how many more use cases people have found for it since then. At the time it was pretty focused on solving our problem of orchestrating a largely static DAG of SQL jobs. It could do other stuff even then, but that was mostly what we were using it for. Airflow has become a victim of its success as it's expanded to meet every problem which could ever be considered a data workflow. The flaws and horror stories in the post and comments here definitely resonate with me. Around the time Airflow was opensource I starting working on data-centric approach to workflow management called Pachyderm[0]. By data-centric I mean that it's focused around the data itself, and its storage, versioning, orchestration and lineage. This leads to a system that feels radically different from a job focused system like Airflow. In a data-centric system your spaghetti nest of DAGs is greatly simplified as the data itself is used to describe most of the complexity. The benefit is that data is a lot simpler to reason about, it's not a living thing that needs to run in a certain way, it just exists, and because it's versioned you have strong guarantees about how it can change.
[0] https://github.com/pachyderm/pachyderm
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One secret tip for first-time OSS contributors. Shh! 🤫 don't tell anyone else
Here is a demo run of lgtm on pachyderm
- Dud: a tool for versioning data alongside source code, written in Go
What are some alternatives?
functime - Time-series machine learning at scale. Built with Polars for embarrassingly parallel feature extraction and forecasts on panel data.
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
OpenVoice - Instant voice cloning by MyShell.
trivy - Find vulnerabilities, misconfigurations, secrets, SBOM in containers, Kubernetes, code repositories, clouds and more
tsflex - Flexible time series feature extraction & processing
dud - A lightweight CLI tool for versioning data alongside source code and building data pipelines.
hamilton - Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage and metadata. Runs and scales everywhere python does.
beneath - Beneath is a serverless real-time data platform ⚡️
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
typhoon-orchestrator - Create elegant data pipelines and deploy to AWS Lambda or Airflow
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
tsuru - Open source and extensible Platform as a Service (PaaS).