patterns-components VS orchest

Hey HN, I’m Ken, co-founder of Patterns (https://www.patterns.app/) with with my friend Chris. Patterns gets rid of repetitive gruntwork when building and deploying data applications. We abstract away the micro-management of compute, storage, orchestration, and visualization, letting you focus on your specific app’s logic. Our goal is to give you a 10x productivity boost when building these things. Basically, we’re Heroku for AI apps. There’s a demo video here: https://www.patterns.app/videos/homepage/demo4k.mp4.

We built Patterns because of our frustration trying to ship data and AI projects. We are data scientists and engineers and have built data stacks over the past 10 years for a wide variety of companies—from small startups to large enterprises across FinTech, Ecommerce, and SaaS. In every situation, we’ve been let down by the tools available in the market.

Every data team spends immense time and resources reinventing the wheel because none of the existing tools work end-to-end (and getting 5 different tools to work together properly is almost as much work as writing them all yourself). ML tools focus on just modeling; notebook tools are brittle, hard to maintain, and don’t help with ETL or operationalization; and orchestration tools don’t integrate well with the development process.

As a result, when we worked on data applications—things like a trading bot side-project, a risk scoring model at a startup, and a PLG (product-led growth) automation at a big company—we spent 90% of our time doing things that weren’t specific to the app itself: getting and cleaning data, building connections to external systems and software, and orchestrating and productionizing. We built Patterns to address these issues and make developing data and AI apps a much better experience.

At its core, Patterns is a reactive (i.e. automatically updating) graph architecture with powerful node abstractions: Python, SQL, Table, Chart, Webhook, etc. You build your app as a graph using the node types that make sense, and write whatever custom code you need to implement your specific app.

We built this architecture for modularity, composability, and testability, with structurally-typed data interfaces. This lets you build and deploy data automations and pipelines quickly and safely. You write and add your own code as you need it, taking advantage of a library of forkable open-source components—see https://www.patterns.app/marketplace/components and https://github.com/patterns-app/patterns-components.git .

Patterns apps are fully defined by files and code, so you can check them into Git the same way you would anything else—but we also provide an editable UI representation for each app. You work at either level, depending on what’s convenient, and your changes propagate automatically to the other level with two-way consistency.

One surprising thing we’ve learned while building this is that the problem actually gets simpler when you broaden the scope. Individual parts of the data stack that are huge challenges in isolation—data observability, lineage, versioning, error handling, productionizing—become much easier when you have a unified “operating system”.

Our customers include SaaS and ecommerce co’s building customer data platforms, fintech companies building lending and risk engines, and AI companies building prompt engineering pipelines.

Here are some apps we think you might like and can clone:

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