Seaweed File System VS Apache Hadoop

FYI: Planning to move from github.com/chrislusf/seaweedfs to github.com/seaweedfs/seaweedfs in the coming days. It may cause some problem for package reference, building, documents, and links. Sorry for the change!

Besides storage itself, S3 API access cost can be high if frequently accessed. And latency is unpredicatble.

You can use SeaweedFS Remote Object Store Gateway to cache S3 (or any S3 API compatible vendors) to local servers, and access them at local network speed, and asynchronously sync back to S3.

https://github.com/chrislusf/seaweedfs/wiki/Gateway-to-Remot...

If you are looking at MinIO you might find SeaweedFS interesting as well.

I believe what you want is a horizontally scalable object store with tiered storage. SeaweedFS is free / open source https://github.com/chrislusf/seaweedfs

I don't know if it fits all of your requests, but you can take a look at seaweedfs, which is pretty good

Seaweedfs deserves a mention here for comparison as well.

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.