Here's a few things that makes this converter different: - runs in the browser - all parsing and conversion is client side can handle data any size data - automatically detects delimiters, encodings, and data types as it parses - Live preview with column renaming, search/replace, and data cleanup - Export to JSON or XML — clean, structured output that can be used for API or Databases
backstory: I built this tool for myself. I work with massive CSV and TXT files, some over 10GB, and opening them in Excel would freeze my laptop, some of the online converters only limits to a certain size, so I started learning Python and pandas but ended up wasting so much time trying different delimiters or fixing badly structured data just to make it usable, and I thought this would be a really fun project to build
I'd love some feedback. Thank you
URL: https://csvforge.com
Shameless plug: I am working on a similar problem of Excel not being a great tool for large datasets. My desktop app[1] lets you import raw data files and query them using SQL. (The website needs to be updated, the app looks much better than the current screenshots).
If so, how much time did it take you?
A couple of kb of open standard vanilla js that does some simple things faster than legacy spreadsheets etc ever could.
Even to the point of creating invoices, reports etc based on standard filters stored in local storage…
Doing this with any kind of data you don't fully own (e.g. data from your company) is a terrible idea, from so many standpoints. That it is "allegedly" running locally is not making it much better.
I think my question to OP is, who is this for. Any developer can write up a convert for his own datasets, in basically any case I can think of where you are handling large amounts of data you are building a pipeline to do cleanup, renaming, conversion, etc. Who wants to have a part of that pipeline be uploading the data into the browser?