orchest VS Sentry

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

Using APM tools like NewRelic, Sentry, Datadog, etc to monitor the performance of your application and while you're on it, they can help you identify N+1 queries.

However, ensuring the reliability and performance of your Next.js app is equally crucial. That’s where Sentry comes into play. Combined with Sentry, an industry-leading error monitoring platform, Next.js empowers developers to proactively identify and resolve issues that may arise in their applications. In this article, we’ll explore how to integrate Sentry into your Next.js project for effective error monitoring and performance optimization.

Sentry: Error monitoring for applications, including APIs. Also offers application performance monitoring (APM).

Indeed, webapps are not immune to distribution problems. Wayward and invasive browser extensions are a clear threat, as are 3rd-party dependencies (and their dependencies) loaded at runtime. Which is why companies like https://sentry.io exist. I think the difference is that webapps are "distributable by default" and it takes real work to break this. Versus having local desktop apps which require work to distribute. A potent example of the power of defaults.

Sentry produces nothing of value? You don't value an open source error tracking and performance monitoring platform? https://github.com/getsentry/sentry

> You invent something, and then immediately turn it into a cheap commodity by releasing it for free.

Exactly. A 71-line python script https://github.com/getsentry/sentry/commit/3c2e87573d3bd16f6... was groundbreaking when it came out and the fact that it springboarded into a startup is commendable.

sentry.io: 5

Sentry - Open Source Alternative For Error Tracking

11. Sentry