Kong VS orchest

Kong: A cloud-native, fast, scalable, and distributed Microservice Abstraction Layer (also known as an API Gateway or API Middleware). Made available as an open-source project in 2015, its core functionality is written in Lua and it runs on the nginx web server.

OPeNsOuRcE. Good time to switch to Kong, better option anyways.

Sounds like you want an API gateway? What about Kong?

Unquestionably no, Kong is "OpenResty plus a management plane" and they're Apache 2: https://github.com/kong/kong#license

Kong (https://konghq.com) | Gateway Senior Engineer | REMOTE Europe | Full-time

The Kong Gateway is an API Management solution, which serves as a foundation for many other solutions by the company. The business model is open-core: an Open Source solution exists (https://github.com/kong/kong), and there's an Enterprise version with more features and dedicated support.

The tech stack is a modified Openresty with of Lua code on top. The ideal candidate would be someone who is already familiar with Kong. Alternatively, if you are familiar with Openresty or other API management solution, we also would love to talk with you.

I am personally interested in finding people to join me in the European Gateway Team. The role involves adding features, fixing bugs, and collaborating with other teams. Here's that position:

https://jobs.lever.co/kong/c1a2b204-45a8-4c19-9cd4-d9824a778...

We have many projects and many teams all around the world (current headcount is ~450), using other technologies like Node in the Kong Manager or Go in the Koko project, and we are constantly looking for people. Please visit our careers page to find out more!

https://konghq.com/careers/

Kong Gateway allows the complexity of service-tier APIs to be reduced to a collection of endpoints (or URIs) focused on meeting a collection of business needs and functionality. Often-duplicated components (like authentication, logging, and security) are handled by the gateway and can be removed from the service-tier design.

I am seeing different companies taking different approach. I am not sure anymore where each should be actually used. On top of that tech like Kong make me question whether API Gateway should be one thing for all. Some perspective into this would be really appreciated.

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