Add your FEATHER file
Drop one or more .feather files onto the converter above, or browse for them. They load into browser memory only — nothing is uploaded, so there is no size-based pricing and no server queue.
Converting a .feather font to .json repackages the same glyphs and metrics for a different delivery target. Free, private, and validated — the file never leaves your browser.
Batch files can each use a different output. Nothing uploads for local conversions.
Working inputs include camera RAW, browser-local audio/video, PDF, CBZ/CBR comics, office documents, ebooks, markup, 3D models, structured text, images, and archives.Converting a .feather font to .json repackages the same glyphs and metrics for a different delivery target. A .feather file is a fast on-disk table format for data frames. Since version 2 it is literally the Apache Arrow IPC file format under a different extension, so a modern .feather file and a modern .arrow file are the same bytes with different names.
A .json file holds JavaScript Object Notation: nested objects, arrays, strings, numbers, booleans, and null written in a strict text syntax. It is the default data language of the web — most APIs speak nothing else. For this route the practical draw is parsers ship in the standard library of essentially every language and readable by humans and machines alike — balanced against no comments — a perpetual annoyance for configuration files, which is worth knowing before you commit a large batch.
The practical trigger for this conversion is usually a mismatch: with .feather, the v1/v2 split still surprises people opening old files. Switching to .json buys you parsers ship in the standard library of essentially every language, which is why it is the better fit for rEST and HTTP API request and response payloads. Because the conversion runs locally, trying it costs nothing but a few seconds of compute on your own machine.
| Aspect | Feather data (.feather) | JSON data (.json) |
|---|---|---|
| Format type | Lossless — every pixel or sample is preserved exactly | Text-based — characters and structure, so there is no visual quality loss |
| How it stores data | a v2 file starts with the ARROW1 magic; a v1 file starts with FEA1, which is how the two are told apart | exactly six value types; no dates, no comments, no trailing commas — the strictness is deliberate |
| Strongest at | moving a data frame between R and Python at native speed | rEST and HTTP API request and response payloads |
| Weak spot | the v1/v2 split still surprises people opening old files | no comments — a perpetual annoyance for configuration files |
Drop one or more .feather files onto the converter above, or browse for them. They load into browser memory only — nothing is uploaded, so there is no size-based pricing and no server queue.
Select .json in the output menu next to each file. The menu only offers targets this engine can genuinely produce, so if JSON is selectable, the route is real and validated.
Press Convert. The font tables are parsed and rewritten locally with fonteditor-core; glyph outlines, hinting, and kerning survive the trip.
Each result is signature-checked before the download unlocks, so a failed encode can never masquerade as a valid JSON file. Outputs keep the original filename with the .json extension.
There is no visual quality to lose — feather is lossless-oriented and json is text-oriented, so the question is structural fidelity. Text, ordering, and basic structure are preserved; complex layout, embedded objects, and styling beyond the target's model are simplified.
Yes — the .feather file is processed inside your browser tab and never uploaded. The font tables are parsed and rewritten locally with fonteditor-core; glyph outlines, hinting, and kerning survive the trip. Close the tab and the file is gone from memory.
Browsers parse it natively through JSON.parse, every mainstream language bundles support, and most editors validate and highlight it out of the box. Viewing a raw .json file is possible in any text editor or browser tab.
It depends on the content: exactly six value types; no dates, no comments, no trailing commas — the strictness is deliberate. Convert one representative file first and compare before batch-processing a large set.
In .feather, schema and column-level metadata are read from the Arrow footer; text and spreadsheet targets have no equivalent channel, so type information is reported in the conversion summary. Re-encoding through the browser pipeline does not carry embedded metadata into the output, which doubles as a privacy scrub — check the exported file if you specifically need tags preserved.
Yes — the reverse route exists as a separate tool. Bear in mind that round-tripping .feather → .json → .feather is not a perfect undo when any lossy step is involved; keep your original if fidelity matters.