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I built CSV GB+ by Data.olllo, a local data tool that lets you open, clean, and export gigabyte-sized CSVs (even billions of rows) without writing code.

Most spreadsheet apps choke on big files. Coding in pandas or Polars works—but not everyone wants to write scripts just to filter or merge CSVs. CSV GB+ gives you a fast, point-and-click interface built on dual backends (memory-optimized or disk-backed) so you can process huge datasets offline.

Key Features: Handles massive CSVs with ease — merge, split, dedup, filter, batch export

Smart engine switch: disk-based "V Core" or RAM-based "P Core"

All processing is offline – no data upload or telemetry

Supports CSV, XLSX, JSON, DBF, Parquet and more

Designed for data pros, students, and privacy-conscious users

Register for 7-days free to pro try, pro versions remove row limits and unlock full features. I’m a solo dev building Data.olllo as a serious alternative to heavy coding or bloated enterprise tools.

Download for Windows: https://apps.microsoft.com/detail/9PFR86LCQPGS

User Guide: https://olllo.top/articles/article-0-Data.olllo-UserGuide

Would love feedback! I’m actively improving it based on real use cases.

Comments (10)

paddy_m · 2h ago

Do you have a demo video?

What are you using for processing (polars)?

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".

As an implementation note, I would make it simple to operate on just the first 1000 (10k or 100k) rows so responses are super quick, then once the users are happy about the transform, make it a single click to operate on the entire file with a time estimate.

Another feature I'd like in this vein is execute on a small subset, then if you find an error with a larger subset, try to reduce the larger subset to a small quick to reproduce version. Especially for deduping.

TheTaytay · 2h ago

Thank you for this. I find myself increasingly using CSVs (TSVs actually) as the data format of choice. I confess I wish this was written for Mac too, but I like the trend of (once again) moving data processing down to our super computers on our desk...

RyanHamilton · 36m ago

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.

hilti · 1h ago

… 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

xnx · 3h ago

Is this better than the free Tad (https://www.tadviewer.com/) which seems to do similar things for free?

rad_gruchalski · 2h ago

And on operating systems other than Windows...

dangerlibrary · 2h ago

It is 2025 and CSVs still dominate data interchange between organizations.

https://graydon2.dreamwidth.org/193447.html

esafak · 2h ago

parquet is also popular.