DataTray

DataTray exists for a specific, very common frustration: the file that is too big for Excel. Open a few million rows and the spreadsheet either refuses outright or becomes unusable, and the standard workarounds — a Python script, a database import, a cloud data tool — each demand either technical skill or a willingness to upload the data somewhere. DataTray takes the third path: a desktop application that processes everything locally, with no row limit and no upload step. It reads CSV, Excel, Parquet and JSON, converts between them, and handles the unglamorous work that occupies most of a data cleanup: profiling a file so you can see column types, null rates and distributions before you trust it; deduplication with a report of what was removed rather than a silent purge; masking and anonymising personally identifiable fields; running SQL over the file directly; and flagging schema drift when this month's export no longer matches last month's. The local-only architecture is the real argument for it. Anyone handling customer records, HR data or anything covered by GDPR or an internal data policy usually cannot paste that file into a browser tool, which rules out most of the convenient options. Processing on your own machine sidesteps the question entirely. The trade-offs are the usual desktop ones: Windows only, performance is bounded by your own hardware, and there is no collaborative layer — this is a single-operator tool, not a shared workspace. For an analyst who periodically needs to make a large, messy file usable and cannot send it off-premises, that is an acceptable shape.