owid-grapher
orchest
owid-grapher | orchest | |
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198 | 44 | |
1,320 | 4,022 | |
1.0% | 0.1% | |
10.0 | 4.5 | |
4 days ago | 11 months ago | |
TypeScript | TypeScript | |
MIT License | 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.
owid-grapher
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IT Healthcare: Its Importance, Challenges And How To Find Good Healthcare Data
Letâs begin with a data visualization-friendly resource.
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Why Are Older Americans Drinking So Much?
Here's a dashboard: https://ourworldindata.org/
Pick almost anything to see a positive trend.
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Observable 2.0, a static site generator for data apps
I think the idea of Framework is really good, but static data limits the applications, excluding monitoring and other cases in which the data is constantly changing, but the dashboard can stay as it is. For example, I'd love to see a revamped Framework version of the LHC beam monitor and related pages (see https://op-webtools.web.cern.ch/vistar/, but check again in 2 months or so, when the accelerator will be running).
In high-energy physics, ROOT is /the/ toolkit for data analysis, and I guess jsROOT (https://root.cern.ch/js/) could also be used to load data to be shown in Framework dashboards. I thought the idea of Framework as a blogging engine with powerful data visualization built-in could be very interesting. Think, for example, about physicists pulling open data (https://opendata.cern.ch) and writing about their analysis or someone pulling data from https://ourworldindata.org/ in their own visualizations to support their case while writing about a particular subject, etc.
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When I look into the future I see nothing.
This is patently false. Visit ourworldindata.org and look at the data for the past few hundred years. 17th-century English philosopher Thomas Hobbes famously wrote the "the life of man [is] solitary, poor, nasty, brutish, and short," which was largely accurate in the 17th century. Today, the poorest people in developed nations enjoy a standard of living that royalty of Hobbes time would have envied. And while the percentage of humanity living in extreme poverty increased from 8.5% to just above 9% in 2022, overall it's down from 80% in the year 1800. We have made similar strides in the areas of education and healthcare.
- The Techno-Optimist Manifesto
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This single dad makes $75K a year. He can't find affordable housing in Vancouver for him and his son
If your statement were true, we wouldn't be living in a world where every measure of human well being only goes up.
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Project Ideas!! Need Guidance
I don't have any ideas, but I'm just sharing this in case you're not aware https://ourworldindata.org/
- Ein tatsächlich guter Artikel Ăźber Fleischersatzprodukte. âWas Sie Ăźber Fleischersatzprodukte wissen solltenâ
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53% of parents say climate change affects their decision to have more kids
Not according to Worldometers.info, nor by ourworldindata.org or worldpopulationreview.com. Wikipedia gives India a slight edge.
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The global trade of plastic waste [OC]
The data comes from ourworldindata.org and from the OECD website. Pretty simple !
orchest
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Decent low code options for orchestration and building data flows?
You can check out our OSS https://github.com/orchest/orchest
- Build ML workflows with Jupyter notebooks
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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).
- Ideas for infrastructure and tooling to use for frequent model retraining?
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Looking for a mentor in MLOps. I am a lead developer.
If youâd like to try something for you data workflows thatâs vendor agnostic (k8s based) and open source you can check out our project: https://github.com/orchest/orchest
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Is there a good way to trigger data pipelines by event instead of cron?
You can find it here: https://github.com/orchest/orchest Convenience install script: https://github.com/orchest/orchest#installation
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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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Launch HN: Sematic (YC S22) â Open-source framework to build ML pipelines faster
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
What are some alternatives?
seaborn - Statistical data visualization in Python
docker-airflow - Docker Apache Airflow
plotly - The interactive graphing library for Python :sparkles: This project now includes Plotly Express!
hookdeck-cli - Receive events (e.g. webhooks) in your development environment
prettymaps - A small set of Python functions to draw pretty maps from OpenStreetMap data. Based on osmnx, matplotlib and shapely libraries.
ploomber - The fastest âĄď¸ way to build data pipelines. Develop iteratively, deploy anywhere. âď¸
nexe - đ create a single executable out of your node.js apps
n8n - Free and source-available fair-code licensed workflow automation tool. Easily automate tasks across different services.
abstreet - Transportation planning and traffic simulation software for creating cities friendlier to walking, biking, and public transit
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
deno - A modern runtime for JavaScript and TypeScript.
Node RED - Low-code programming for event-driven applications