JSON, YAML, CSV, and TOML: When to Use Each Data Format
Four data formats, four different jobs. A practical guide to what each one is good at, where each falls apart, and how to convert cleanly between them.
Software spends a surprising amount of its life just moving structured data around: an API returns JSON, a config file is written in YAML or TOML, a report is exported as CSV, a spreadsheet wants tabular rows. These formats are not interchangeable — each was designed for a particular job, and using the wrong one creates friction. Knowing the strengths of each makes you faster and saves you from a category of frustrating bugs.
JSON: the lingua franca of APIs
JSON (JavaScript Object Notation) is the default for data exchange between systems, especially web APIs. It represents nested objects and arrays cleanly, every programming language can parse it, and its rules are strict enough to be unambiguous. That strictness is also its main friction for humans: no comments are allowed, every string needs double quotes, and a single trailing comma makes the whole document invalid. JSON is excellent for machine-to-machine communication and data storage; it is merely tolerable for files humans have to edit by hand.
YAML: configuration humans edit
YAML was designed to be readable and writable by people. It uses indentation instead of braces, supports comments, and drops most of the punctuation that makes JSON noisy. This makes it popular for configuration in tools like CI pipelines and container orchestration. Its strength is also its danger: because structure is defined by indentation, a single misplaced space can silently change the meaning of your file or break it entirely. YAML also has surprising type-coercion quirks (the classic example: the word "no" being read as the boolean false). Use YAML for human-edited configuration, but validate it.
TOML: configuration that stays unambiguous
TOML (Tom’s Obvious Minimal Language) aims for YAML’s readability without YAML’s ambiguity. It uses explicit, INI-like sections and clear key-value pairs, supports comments, and has unambiguous typing. It is less prone to the silent indentation mistakes that plague YAML, which is why a number of modern tools have adopted it for their config files. It is excellent for flat-to-moderately-nested configuration; deeply nested data structures become more awkward in TOML than in JSON or YAML.
CSV: flat, tabular data
CSV (Comma-Separated Values) is the simplest of the four and the right answer for one specific shape of data: flat tables of rows and columns, like a spreadsheet export. It is universally understood by spreadsheet software, databases, and data tools. Its limitation is that it has no concept of nesting — you cannot represent an object-within-an-object cleanly. It also has real-world messiness around quoting, commas inside values, and inconsistent line endings. Use CSV for tabular data destined for spreadsheets or simple import/export; do not try to force hierarchical data into it.
Converting between them
Conversions are common and mostly straightforward, with one thing to watch: structural mismatch. JSON, YAML, and TOML can all represent nested data, so converting among them is usually clean. CSV is flat, so converting nested JSON to CSV requires flattening — a nested key like "user.address.city" typically becomes a column header using dot notation. Going the other way, from CSV to JSON, gives you a flat array of objects. Understanding this is the key to not being surprised by the output.
- →API responses and data interchange → JSON.
- →Human-edited config in pipelines and orchestration → YAML (and validate it).
- →Human-edited config you want to stay unambiguous → TOML.
- →Flat tabular data for spreadsheets → CSV.
Our Data Converter handles bidirectional conversion between JSON, CSV, YAML, and TOML right in your browser, validating the input and flattening or expanding structure as needed. If you just need to clean up and validate a single format — pretty-printing minified JSON, or catching a syntax error in a config file — the Formatter is the faster tool. And when your source or destination is a real spreadsheet file, the Spreadsheet Converter bridges that gap. As always, everything runs locally, so your data files never leave your machine.