docker-airflow
orchest
Our great sponsors
docker-airflow | orchest | |
---|---|---|
10 | 44 | |
3,703 | 4,016 | |
- | 0.2% | |
0.0 | 4.5 | |
about 1 year ago | 10 months ago | |
Shell | TypeScript | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
docker-airflow
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Amount of effort to stand up, integrate and manage a small airflow implementation
Used a custom version of Puckel Airflow Docker image (Spent a lot of time customising to our needs, but default Airflow container should still work)
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The Unbundling of Airflow
I understand it is subjective. But I use a forked version of https://github.com/puckel/docker-airflow on our managed K8s cluster and it points to a cloud managed Postgres. It has worked pretty well for over 3 years with no-one actually managing it from an infra POV. YMMV. This is driving a product whose ARR is well in the 100s of Millions.
If you have simple needs that are more or less set, I agree Airflow is overkill and a simple Jenkins instance is all you need.
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ETL com Apache Airflow, Web Scraping, AWS S3, Apache Spark e Redshift | Parte 1
A imagem do docker utilizada foi a puckel/docker-airflow onde acrescentei o BeautifulSoup como dependĂȘncia para criação da imagem em minha mĂĄquina.
orchest
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Building container images in Kubernetes, how would you approach it?
The code example is part of our ELT/data pipeline tool called Orchest: https://github.com/orchest/orchest/
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Launch HN: Patterns (YC S21) â A much faster way to build and deploy data apps
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
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Argo became a graduated CNCF project
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).
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How do you deal with parallelising parts of an ML pipeline especially on Python?
We automatically provide container level parallelism in Orchest: https://github.com/orchest/orchest
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Prefect vs other things question
If youâre looking for something with a great UI experience you can check out our open source project called Orchest. It might be what you seek from a simplicity perspective. https://github.com/orchest/orchest
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Airflow's Problem
Argo is pretty amazing if you want to take advantage of the work Kubernetes has done to scale resource efficiently across a cluster of compute nodes.
If youâre looking for something thatâs a bit more high level and friendly to expose directly to your data team (data scientists/data engineers/data analysts) you can check out https://github.com/orchest/orchest
You can think of it as a browser UI/workbench for Argo scheduled pipelines. Disclaimer: author of the project
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How are you guys validating your data?
+1 on a lightweight version of GE to more easily make part of an existing pipeline. Would like it for internal use (our data pipelines), but also for our open source users (https://github.com/orchest/orchest).
- Apache Hop 2.0
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I reviewed 50+ open-source MLOps tools. Hereâs the result
You might want to add https://github.com/orchest/orchest/ to the Pipeline orchestration category (disclaimer: I work at the company making it)
What are some alternatives?
ploomber - The fastest âĄïž way to build data pipelines. Develop iteratively, deploy anywhere. âïž
hookdeck-cli - Manage your Hookdeck workspaces, connections, transformations, filters, and more with the Hookdeck CLI
n8n - Free and source-available fair-code licensed workflow automation tool. Easily automate tasks across different services.
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
Node RED - Low-code programming for event-driven applications
ExpansionCards - Reference designs and documentation to create Expansion Cards for the Framework Laptop
parabol - Free online agile retrospective meeting tool
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
PostHog - đŠ PostHog provides open-source product analytics, session recording, feature flagging and A/B testing that you can self-host.
wordpress-docker-compose - Easy Wordpress development with Docker and Docker Compose
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows