Tahoe-LAFS VS Apache Hadoop

You could also look at Tahoe-LAFS which I keep meaning to try: https://tahoe-lafs.org/

Then there are Darcs and Pijul, which use a theory of patches.

So Pijul manages to have lossless merges by actually storing a directed graph (though of course, you will still need to decide how to flatten that into a displayed file) :

https://jneem.github.io/pijul/

And because uses more information about the history, it is able to do smarter merges (if I am not mistaken, even compared to the OP ?) :

https://tahoe-lafs.org/~zooko/badmerge/simple.html

https://pijul.org/faq

Good news for you: I'm well on the way to solving the problem of better code merging. Specifically, the algorithms I am developing appear to be able to do a correct merge on both [1] and [2]. They also appear capable of merging binary data.

The tradeoff is that people need to write some code to tell the VCS about the format of each binary file type or semantics of each programming language.

The biggest problem is that, like Rust, a new VCS has to be well-executed to make its innovation stick. We'll see if I succeed.

[1]: https://tahoe-lafs.org/~zooko/badmerge/simple.html

[2]: https://tahoe-lafs.org/~zooko/badmerge/concrete-bad-semantic...

u/nextale shared a couple options: Tahoe-LAFS , Duplicati an Retroshare

Only thing that comes close is https://tahoe-lafs.org

There is Tahoe-LAFS which is decentralized open-source software where you can add remote storage servers (for example on a friends server) to store your data but the server does not have the encryption keys. Your data is encrypted before it leaves your computer (they call it "Least Authority File System" or LAFS because only you hold the keys, the storage servers just store the data). The data is encrypted in-transit and on-rest and supports multiple nodes so even if one of the servers burn down you still have the same data elsewhere. I believe they offer a commercial storage solution but you and your friends could install it for yourselves and run a closed network.

do you know https://tahoe-lafs.org? your goals sound similar.

Did you check out tools like https://hadoop.apache.org/ ?

check out the various dockerfiles bundled with hadoop on GitHub. you can point to them from within docker-compose. they haven't been updated in a couple years tho.

There are different ways to implement parallel dataflows, such as using parallel data processing frameworks like Apache Hadoop, Apache Spark, and Apache Flink, or using cloud-based services like Amazon EMR and Google Cloud Dataflow. It is also possible to use parallel dataflow frameworks to handle big data and distributed computing, like Apache Nifi and Apache Kafka.

There are several frameworks available for batch processing, such as Hadoop, Apache Storm, and DataTorrent RTS.

A copy of Hadoop installed on each of these machines. You can download Hadoop from the Apache website, or you can use a distribution like Cloudera or Hortonworks.

The Apache™ Hadoop™ project develops open-source software for reliable, scalable, distributed computing.

So, clearly with your AP CS class and PLC logic knowledge, if you were dumped into a codebase like Hadoop, QT, or TensorFlow you'd be able to quickly and competently analyze what is going on with that code, understand all the libraries used, know the reasons why certain compromises were made, and be able to make suggestions on how to restructure the code in a different way? Because I've been programming for coming up on two decades and unless a system is within the domains that I have experience in, I would not be able to provide any useful information without a massive onboarding timeline, and definitely wouldn't be able to help redesign anything until actually coding within the system for a significant amount of time.

This requires the use of distributed computation tools such as Spark and Hadoop, Flink and Kafka are used. But for occasional experimentation, Pandas, Geopandas and Dask are some of the commonly used tools.