docs: rework why Ibis article to explain what Ibis is and other updates #8490
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also:
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gforsyth
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This is really great, @lostmygithubaccount -- that tabset code sample at the top is especially clean.
Some small nits and some suggestions for (all of us for) the future, but I like the categorization and presentation here.
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| Out of the box, Ibis offers a great local experience for working with many file | ||
| formats. | ||
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| DuckDB is the default backend, with Polars and DataFusion as two other great | ||
| local options. Many of the backends can run locally but require more setup than | ||
| a pip installation. | ||
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| ### Scaling up and out | ||
| Ibis already works with other Python dataframes like: | ||
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| After prototyping on a local backend, directly scale in the cloud. | ||
| - [pandas](https://github.com/pandas-dev/pandas), | ||
| - [Dask](https://github.com/dask/dask) | ||
| - [Polars](https://github.com/pola-rs/polars) | ||
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| You can prototype on DuckDB and deploy with MotherDuck. You can scale from any | ||
| Python client with Ibis installed to whatever your backend supports. | ||
| Ibis already works well with visualization libraries like: | ||
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| ## Use cases | ||
| - [matplotlib](https://github.com/matplotlib/matplotlib) | ||
| - [seaborn](https://github.com/mwaskom/seaborn) | ||
| - [plotly](https://github.com/plotly/plotly.py) | ||
| - [altair](https://github.com/altair-viz/altair) | ||
| - [plotnine](https://github.com/has2k1/plotnine) | ||
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| You can use Ibis at any stage of your data workflow. | ||
| Ibis already works well with dashboarding libraries like: | ||
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| Use the same framework for local exploration on a few files or production | ||
| workloads on the most advanced data platforms. | ||
| - [Streamlit](https://github.com/streamlit/streamlit) | ||
| - [dash](https://github.com/plotly/dash) | ||
| - [Quarto dashboards](https://github.com/quarto-dev/quarto-cli) | ||
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| Ibis helps with: | ||
| Ibis already works well with machine learning libraries like: | ||
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| - data catalog exploration | ||
| - exploratory data analysis | ||
| - transforming data | ||
| - visualizing data | ||
| - data science and machine learning | ||
| - [scikit-learn](https://github.com/scikit-learn/scikit-learn) | ||
| - [XGBoost](https://github.com/dmlc/xgboost) | ||
| - [LightGBM](https://github.com/microsoft/lightgbm) | ||
| - [PyTorch](https://github.com/pytorch/pytorch) |
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Broader documentation comment -- for everything like this that we advertise, I think we should aim to have short how-to docs on using X with Ibis.
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| - prototype with the same API that will be used in production | ||
| - preprocess and feature engineer data before training a machine learning model | ||
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| ### Ibis for data platforms |
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i personally think data platforms/engines are integration partners, not users. For that reason, I almost feel like we should have a separate blog to focus on the whys for partners, to keep this one focused and avoid any potential confusion to users.
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I'll leave this as a follow up blog -- "Ibis for data platforms" -- I think this still makes sense here
| Out of the box, Ibis offers a great local experience for working with many file | ||
| formats. | ||
| As of [Ibis 8.0](./posts/ibis-version-8.0.0-release/index.qmd), the first stream | ||
| processing backends have been added. Since these systems tend to support SQL, we |
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We can describe why unification is possible with a single Python dataframe API. The concept is stream-table duality. https://www.confluent.io/blog/kafka-streams-tables-part-1-event-streaming/
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I'll leave this as a follow-up for someone more confidence in batch/streaming concepts than me!
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there's one more commit (the freeze) that seems lost in the GH incident I guess it'd good? I'm a little confused |
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Description of changes
Issues closed
closes #8251
closes #8488