akvo-flow VS orchest

Akvo | Frontend developer | Remote or Burkina Faso | +- 2hours CET/CEST | Full time | https://akvo.org/join-our-team/

We believe that sustainable change will happen faster if governments and non governmental organisations become more effective, accountable and collaborative. With a team of data and development experts, a solid and scalable open source data platform, and a proven methodology, we help our partners power their decisions to achieve inclusive and sustainable impact. We support the improved management of water, sanitation and agriculture and have a strong commitment to the sustainable development goals.

Together with our diverse team, you are driven to create valuable products that enhance Akvo’s impact. You are able to figure things out yourself and know when to involve the rest of the team. You always keep pushing to understand why certain things are the way they are or what the root cause of a problem is while being willing to learn and stay at the forefront of current technologies. You do not shy away from complexity and unknowns, you take initiative, and you understand how to split things up in clever ways, so we ship step-by-step and learn from it quickly.

We value a great eye for good UI, experience with JavaScript, React, Clojure, ClojureScript, Java, Ember and a willingness to learn.

Some projects you will work on is Akvo Lumen (https://github.com/akvo/akvo-lumen) & Akvo Flow (https://github.com/akvo/akvo-flow). To learn more and apply for this position see https://akvo.org/join-our-team/, please send portfolio links, your motivation letter and a resume all in English to the attention of Daniel Sunnerek to [email protected].

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