Gravitational Teleport VS orchest

Teleport - Comprehensive control plane tool, but also supports accessing apps behind NATs. Written in Go.

https://github.com/gravitational/teleport/blob/master/rfd/00...

I ordinarily would have said you reinvented Teleport <https://github.com/gravitational/teleport/tree/v14.3.7#readm...> but now that they've gone AGPL with v15 I'm guessing there's a market for MIT licensed stuff, although for sure since Teleport has been around for so long it has encountered more edge cases and undergone more security reviews. I was surprised while digging up the link that Gravatational is still releasing v13 and v14 updates under Apache 2, so maybe even Teleport will continue to have legs for those who cannot deploy AGPL stuff

Teleport is an SSH for Clusters and Teams and aims to be the drop-in replacement for OpenSSH.

Are you looking to set up SSO for your cloud infrastructure? Or maybe establish tunnels to access services behind NATs and firewalls. Then Teleport is for you. It provides connectivity, authentication, access controls and audit for infrastructure. The newest update has a tonne of new features and improvements including enhanced device trust support, SSH connection resumption, MFA for admin actions, improved provisioning for Okta, and heaps. more. Check out all the changes in the Teleport release notes.

In case you didn't see it: https://goteleport.com/blog/teleport-oss-switches-to-agpl-v3... and https://github.com/gravitational/teleport/pull/35259

I readily admit it's not the same amount of :fu: as BuSL or whatever the fuck is going on over at Sentry but still :-( as compared to their much friendlier Apache 2

Link to GitHub -->

OP here, thanks for posting! Happy to Answer any questions. I have to give our a shoutout to Alan at Teleport for all his work on Passwordless and his work to make Passwordless / TouchID work with MacOS CLI https://github.com/gravitational/teleport/blob/master/rfd/00...

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