Comparison, written September 2026 September 3, 2026 by Arif Aslam 5 min read

A CSVJSON alternative

CSVJSON does one pair of conversions and does them quickly, with a clean interface and no ceremony. If that is what you need, it is a perfectly good answer and has been for years.

Fair is fair: what CSVJSON does well

Focus. A tool that does one thing has no menu to navigate and no mode to be in, and for a conversion you perform occasionally that is worth a lot. The editor-style layout, with input on one side and output on the other, makes it obvious what is happening.

It also runs in the browser rather than uploading, which was not the norm for converters when it appeared.

Where the two differ

	ExploreMyData	CSVJSON
Type fidelity	One type per column, decided by profiling the whole column	Simple converters often emit values as strings; check the output against your consumer's schema
Nested structures	Objects flattened with dotted paths; arrays handled deliberately, with a guide on the choice	Focused on the flat table case
Direction and dialects	CSV, TSV, JSON, JSONL, YAML and XML in a connected set of pages, with delimiter and encoding options	CSV and JSON, both ways
File size	100 MB stated cap with a named handoff to a 1 GB editor	Editor-style tools are happiest with modest inputs

Competitor details are general and may change; verify current capabilities on their site. Statements about ExploreMyData are testable here, right now.

I used CSVJSON for X. Where is that here?

There	Here
CSV to JSON	CSV to JSON
JSON to CSV	JSON to CSV, nested objects flattened
NDJSON work	CSV to JSONL and JSONL to CSV
Formatting or minifying JSON	JSON Formatter, handles large files
Picking fields out of JSON	JSONPath Tester

The two-minute test

Convert a file that has a numeric column with a few blanks in it, and look at what comes out. Whether those blanks force the whole column to strings, and whether the type is decided per column or per cell, is the thing that will bite you three systems downstream. It is a fair test to run on either tool.

Questions people actually ask

Why does type fidelity matter in CSV to JSON?

Because a simple converter emits every value as a string, and a consumer expecting a number then has to cast every field. Worse is a converter that types per cell rather than per column: the same key becomes a string on one line and a number on the next, and strict consumers like BigQuery reject the load with a schema conflict.

How do you decide a column's type?

By profiling the whole column rather than each value, then applying one type to every row of it. A column that is numeric except for three blanks is numeric with three nulls, not a mixed column.

What happens to nested JSON going to CSV?

Nested objects are flattened with dotted paths, so a value at user.name becomes a column of that name. Arrays are a real modeling decision rather than a setting, and we wrote a guide about choosing between exploding and joining.

Is CSVJSON still available?

It has been, at the time of writing. Availability of small independent tools does change, which is one reason to know an alternative exists rather than to discover it on a deadline.

Do you handle JSON Lines?

Yes, in both directions, on dedicated pages. JSONL is usually easier to convert than a single large JSON array because each record is independent and can be streamed.