JSON and CSV are the two most widely used formats for moving data between systems. They are both plain text, both universally supported, and both free to use. But they are not interchangeable. Choosing the wrong format for a task can mean hours of unnecessary conversion work — or data that simply cannot be represented correctly.
What is CSV?
CSV (Comma-Separated Values) is the simplest data format that exists. Every row is a line of text, every column is separated by a comma. The first row is usually a header row with column names. That is the entire specification.
Alice,30,London
Bob,25,New York
Carol,35,Sydney
Every spreadsheet application on earth — Excel, Google Sheets, LibreOffice Calc — opens CSV files natively. Every database can import and export CSV. It is the universal data handshake format.
What is JSON?
JSON (JavaScript Object Notation) supports nested structures, arrays within arrays, and mixed data types. A single JSON value can represent an entire object graph that would require multiple CSV files to express.
{
"name": "Alice",
"age": 30,
"address": { "city": "London", "zip": "W1A" },
"tags": ["developer", "designer"]
}
]
Notice what CSV cannot represent here: the nested address object, and the tags array with multiple values. JSON handles these naturally.
Side-by-side comparison
| Feature | CSV | JSON |
|---|---|---|
| Nesting / hierarchy | No | Yes |
| Arrays within rows | No | Yes |
| Data types | All strings | String, number, boolean, null, array, object |
| File size | Smaller | Larger (key names repeated per row) |
| Human readability | Very easy | Easy with formatting |
| Excel / Sheets support | Native | Requires plugin or conversion |
| API usage | Uncommon | Universal standard |
| Streaming large datasets | Excellent | Harder (must parse full structure) |
When to use CSV
- Spreadsheet work — any data that will be opened in Excel or Google Sheets
- Data analysis — pandas, R, and most BI tools prefer tabular CSV input
- Simple exports — database table dumps, report exports, mailing lists
- Large flat datasets — millions of rows of simple structured data
- Interoperability — when you don't know what tool will consume the file
When to use JSON
- APIs — virtually every REST API sends and receives JSON
- Nested data — anything with objects inside objects, or arrays of objects
- Preserving types — numbers stay numbers, booleans stay booleans
- Configuration files — package.json, settings files, app config
- Web development — fetching and storing data in JavaScript applications
What gets lost in conversion
When you convert JSON to CSV, nested objects and arrays must be flattened. An address object with city, zip and country becomes three separate columns. An array of tags becomes a single cell containing all values joined by a separator. Some information may not survive the conversion cleanly — this is a fundamental limitation of the flat tabular model.