repo.macintoshgarden.org-fileset VS pachyderm

Sure, how would you like to get in touch? You have a discord, right? I actually was looking at your project and was thinking of opening a simple PR and Issue. (same username)

I have some more examples git-annex repos:

This is an annex repo I made of this popular abandonware website:

https://github.com/unqueued/repo.macintoshgarden.org-fileset

https://github.com/unqueued/ratholeradio-archive

What's cool is that people can use standard pull requests to add files to the repo. And the repo itself is small, but it can represent huge filesets. Datalad has some really fascinating medical imaging data repos that are massive (https://www.datalad.org/datasets.html).

If you wanna see a really good example of a repo with versioned binary files, check this out the git annex repo of previous git-annex binary releases:

https://downloads.kitenet.net/.git/

You can just use standard git workflows to see previous revisions of a file (well, previous hashes) but it is really easy to hook into.

I actually have thought about making a special remote specifically to diff images. But yeah, git-annex does a really amazing job at bridging the bag between binary and git.

20. Pachyderm | Github | tutorial

Pachyderm specializes in creating compliance-focused pipelines that integrate with enterprise-level storage solutions.

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 - License prevents competition.

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

Here is a demo run of lgtm on pachyderm