parquet-wasm VS arquero

nodejs-polars is node-specific and uses native FFI. polars can be compiled to Wasm but doesn't yet have a js API out of the box.

As for the fastest way to serialize data to Pandas data to the browser, you should use Parquet; it's the fastest to write on the Python side and read on the JS side, while also being compressed. See https://github.com/kylebarron/parquet-wasm (full disclosure, I wrote this)

I'm building WebAssembly bindings to existing Rust libraries [0] and lower-dependency geospatial tools [1]. Rust makes it very easy to bind rust code to both WebAssembly and Python. And by avoiding some large C geospatial dependencies we can get reliable performance in both wasm and Python using the exact same codebase.

[0]: https://github.com/kylebarron/parquet-wasm

[1]: https://github.com/kylebarron/geopolars

Looks to be a great resource. I've been working on a WASM implementation of reading and writing Apache Parquet [0] and it's been difficult being new to WASM to find the best way of distributing the WASM that works on Node and through bundlers like Webpack.

[0]: https://github.com/kylebarron/parquet-wasm

Hey HN, I'm excited to share a new side project I've been working on.

The product is called Matrices. You can check it out here: https://matrices.com/.

With Matrices, you can *explore*, *visualize*, and *share* large (100k rows) datasets–all without code. Filter data down to just what you want, visualize it with built-in charts, and share your results with one click.

You can use it today (no login or waitlist or anything). Just copy and paste your data from a google sheet or CSV file.

It's hard to describe the feeling of "gliding over data" you get with Matrices, so I'd rather *show* you how it works instead. This 75s video will give you a sense of how it works: https://www.youtube.com/watch?v=Rrh9_I3Ux8E.

Data is stored locally in your browser until you publish it, though small sample does go to the OpenAI APIs for AI-assisted features.

I started building Matrices because I wanted a tool that made it easy to explore new datasets. When I'm first trying to dig into data, I'll have one question... that leads to another... that will invariably lead to five more questions. It's sort of a fractal process, and I couldn't find many good options that were fast, responsive, and visual.

I figured this crowd would be interested in tech stack as well, it's using arquero [1] bindings over apache arrow for in-memory analytics, and visx [2] for visualizations. I'd like to add duckdb-wasm support at some point to open up a wider set of databases. Data is serialized as parquet to save a bit on bandwidth + storage.

Give it a spin, and let me know what you think. This is my first 'serious frontend project' so I appreciate any and all feedback and bug reports. Feel free to comment here (I'll be around most of the day), or shoot me a note: [email protected]

[1]: https://uwdata.github.io/arquero/

https://github.com/uwdata/arquero

Transformations: We found out that JavaScript in combination with D3.js has a pretty decent set of data transformation functions; however, it comes nowhere near to Pandas or dplyr. We found out about Tidy.js quite early, loved it, and adopted it. The combination of Tidy.js and D3.js and Plot.js is absolutely amazing for visualizations and data wrangling with small datasets, say 10-100K rows. We were very happy with this for a while; however, once you move away from visualizations into real-world data analysis, we found out 100K rows restrictive, which gets worse when having 100 or 1K columns. So we switched gears and started using Arquero.js, which happens to be columnar and enabled us to process +1M rows in the browser, descent size for real-world data analysis.

Take a look at the arquero library from a research group at University of Washington (the same group that D3 came out of). https://github.com/uwdata/arquero