And on operating systems other than Windows...

Tad is a great tool—very clean and useful for quick exploration.

Data.olllo is focused more on local data processing, not just viewing—things like filtering, transforming, merging, and even running Python code (with AI assistance coming). It’s built for both small and large files with performance in mind, using many cores including Polars under the hood.

Also, good news: the macOS version is in the works and will be submitted to the Mac App Store soon!

parquet is also popular.

Absolutely—CSVs are still everywhere, especially for simple interchange between teams and tools. I designed Data.olllo with that in mind.

That said, I also plan to add support for Parquet and other formats soon—definitely agree it's gaining traction for larger, structured datasets.

> Marketing note: I'm sure you're proud of P Core/V Core, but that doesn't matter to your users, it's an implementation detail. At a maximum I'd write "intelligent execution that scales from small files to large files".

Speaking personally, "intelligent execution that scales from small files to large files" sounds like marketing buzz that could mean absolutely nothing. I like that it mentions specifically switching between RAM and disk-powered engines, because that suggests it's not just marketing speak, but was actually engineered. Maybe P vs V Core is not the best way to market it, but I think it's worth mentioning that design.

Thanks for the thoughtful feedback!

Yes, Data.olllo uses including Polars under the hood for fast and efficient processing. A demo video is in the works and should be up soon.

Good point about the "P Core/V Core" naming—I'll simplify that to focus more on the user benefit, like scaling from small to large files smoothly.

I also like your idea of running transformations on a sample first with a one-click full run—very aligned with the vision. And subset reproduction for errors is a great suggestion, especially for things like deduping. Appreciate it!

… I‘m trying to use our super computers in our pockets, like an iPhone ;-) But still struggling with the way how to present CSV data effectively on a small screen, although it‘s huge in terms of pixels compared to computer screens from the 90s

It‘s interesting to research how capable applications like Lotus123 have been even on low resolutions like 800x600 pixel compared to today’s standard

QStudio allows querying CSV on mac via DuckDB: https://www.timestored.com/qstudio/csv-file-viewer I've been improving the Mac version a lot lately, key bindings, icon, an App package to download. So if you find any problems please raise a github issue.

If you are wrangling CSV/TSV files on Mac, it might be worth taking a look at Easy Data Transform.

Ok, if we are all tagging and promoting our own projects, check out mine.

I created Buckaroo to provide a better table viewing experience inside of notebooks. I also built a low code UI and auto cleaning to expedite the wrote data cleaning tasks that take up a large portion of data analysis. Autocleaning is heuristically powered - no LLMs, so it's fast and your data stays local. You can apply different autocleaning strategies and visually inspect the results. When you are happy with the cleaning, you can copy and paste the python code as a reusable function.

All of this is open source, and its extendable/customizable.

Here's a video walking through autocleaning and how to extend it https://youtu.be/A-GKVsqTLMI

Here's the repo: https://github.com/paddymul/buckaroo

Thank you! I completely agree—TSVs/CSVs are such a simple yet powerful format, and it's great to hear you're making good use of them. I'm also a big fan of doing as much as possible locally—our machines are incredibly capable these days. Good news: I'm currently working on the macOS version of Data.olllo and plan to submit it to the Mac App Store soon. Stay tuned!