cozystack VS orchest

It introduces new resources (https://github.com/seaweedfs/seaweedfs-cosi-driver/tree/main...) such as BucketClaim, Bucket, and BucketAccess for the declarative provisioning of S3 buckets and access management based on the PVC principle.

We are working (https://github.com/aenix-io/cozystack/pull/131) on adding support for S3 buckets in Cozystack, and this driver will allow you to automatically order buckets directly from Kubernetes.

Hey, Cozystack developer here!

I'm happy to announce that in our latest release we have stabilized tenant Kubernetes clusters running in the native Kubernetes ecosystem.

Kubernetes was never developed as a multi-tenant solution, so it is intended to have separate Kubernetes for separate projects and groups of developers.

Cozystack runs virtualization using KubeVirt and allows you to spawn extra Kubernetes clusters with just a click.

Just as in any robust cloud, users don't see their control-plane nodes, because it runs as pods managed behind the scenes, thanks to Kamaji project.

Inside these clusters, all cloud features are operational: load balancers, persistent volumes, and cluster autoscaling.

You can try it out yourself by installing the platform on-premises for free. The source code is available on GitHub:

https://github.com/aenix-io/cozystack

You can check out our OSS https://github.com/orchest/orchest

The code example is part of our ELT/data pipeline tool called Orchest: https://github.com/orchest/orchest/

First want to say congrats to the Patterns team for creating a gorgeous looking tool. Very minimal and approachable. Massive kudos!

Disclaimer: we're building something very similar and I'm curious about a couple of things.

One of the questions our users have asked us often is how to minimize the dependence on "product specific" components/nodes/steps. For example, if you write CI for GitHub Actions you may use a bunch of GitHub Action references.

Looking at the `graph.yml` in some of the examples you shared you use a similar approach (e.g. patterns/openai-completion@v4). That means that whenever you depend on such components your automation/data pipeline becomes more tied to the specific tool (GitHub Actions/Patterns), effectively locking in users.

How are you helping users feel comfortable with that problem (I don't want to invest in something that's not portable)? It's something we've struggled with ourselves as we're expanding the "out of the box" capabilities you get.

Furthermore, would have loved to see this as an open source project. But I guess the second best thing to open source is some open source contributions and `dcp` and `common-model` look quite interesting!

For those who are curious, I'm one of the authors of https://github.com/orchest/orchest

Haven't tried it. In its favor, Argo is vendor neutral and is really easy to set up in a local k8s environment like docker for desktop or minikube. If you already use k8s for configuration, service discovery, secret management, etc, it's dead simple to set up and use (avoiding configuration having to learn a whole new workflow configuration language in addition to k8s). The big downside is that it doesn't have a visual DAG editor (although that might be a positive for engineers having to fix workflows written by non-programmers), but the relatively bare-metal nature of Argo means that it's fairly easy to use it as an underlying engine for a more opinionated or lower-code framework (orchest is a notable one out now).

If you’d like to try something for you data workflows that’s vendor agnostic (k8s based) and open source you can check out our project: https://github.com/orchest/orchest

You can find it here: https://github.com/orchest/orchest Convenience install script: https://github.com/orchest/orchest#installation

We automatically provide container level parallelism in Orchest: https://github.com/orchest/orchest

For people in this thread interested in what this tool is an alternative to: Airflow, Luigi, Kubeflow, Kedro, Flyte, Metaflow, Sagemaker Pipelines, GCP Vertex Workbench, Azure Data Factory, Azure ML, Dagster, DVC, ClearML, Prefect, Pachyderm, and Orchest.

Disclaimer: author of Orchest https://github.com/orchest/orchest