Data Tools — Convert and Inspect CSV, JSON and XML Free
Data tools convert and inspect CSV, JSON and XML locally, keeping values as text so codes, identifiers and leading zeros survive a round trip.
All Data tools
- CSV Delimiter Converter
Switch CSV files between comma, semicolon, tab, and pipe delimiters.
- CSV to JSON
Convert CSV rows to a JSON array with header validation.
- CSV Viewer
Browse huge CSV files as a fast table with filter and export.
- JSON to CSV
Flatten a JSON array of objects into spreadsheet-ready CSV.
- JSON Tree Viewer
Browse JSON as a collapsible tree with search and path copy.
- JSON YAML Converter
Convert JSON to YAML or YAML to JSON locally, with readable indentation and clear parse errors.
- Text to CSV
Turn plain pasted lines into structured CSV rows and columns.
- XML Formatter & Validator
Pretty-print and validate XML with entity-safe local parsing.
- XML to JSON
Convert XML to JSON under an explicit, documented mapping policy.
- Quick Chart Maker
Interactive quick chart maker to design custom bar, line, pie, and radar graphs with CSV import and high-res vector export locally.
- Mock Data Generator
Generate realistic test records and database schemas with our mock data generator. Export English and Arabic datasets to JSON, CSV, and SQL scripts.
Tabular and structured files carry the real work of a business, yet each system prefers a different dialect. Analysts receive comma-separated exports, services exchange JSON, and older tools insist on XML, so someone must convert, inspect or repair the data in between. This set covers that middle ground. Change delimiters, swap CSV for JSON and back, turn pasted lines into a table, browse enormous spreadsheets as a searchable grid, expand JSON into a collapsible tree, and map XML into JSON under a documented policy. Headers are validated, values remain text so identifiers such as postal codes survive, and nothing is retyped silently. The pages help data engineers, marketers, researchers and support staff who need to look at a file before trusting it. Parsing is confined to your device: spreadsheets and structured documents are never uploaded, and no sample row is stored or reused. Journalists cross-checking an export, or support agents pasting tickets, can inspect the structure without a spreadsheet program installed.
Formats and limits are described per tool; each tool page states the ones that tool actually accepts and enforces.
Privacy: Spreadsheets and structured files are parsed inside your browser. The rows you open are never uploaded, and no sample value is stored or reused after the tab closes.
Looking before you convert
The routine here is checking a dataset before anyone trusts it. Open the file first and browse the table to confirm its headers and shape, because a wrong delimiter fools every later step. Once the columns look sane, convert the rows into JSON objects for a service, or reverse the journey on the JSON-to-CSV page. When a system insists on another separator, switch the delimiter without touching the values so identifiers survive. Nested payloads are easier to inspect by expanding the JSON as a collapsible tree than by scrolling raw braces.
The classic blunder is converting before looking, which cheerfully turns a semicolon file into one column of nonsense. People also let spreadsheets retype values, quietly stripping leading zeros from codes, or assume a tidy round trip between XML and JSON when the mapping policy says otherwise. Encoding is another silent trap: a Latin-1 export read as UTF-8 produces the wrong accents. Check the first and last rows, confirm the encoding, and treat every conversion as something to verify rather than trust.
Pick by transformation. Inspecting wants a viewer, changing separators wants the delimiter tool, moving between formats wants the relevant converter, and nested documents want the tree view. Text that merely looks tabular usually belongs to the text-to-CSV page until it is genuinely structured.
Good habits start with a sample. Skim the opening rows, the closing rows and any suspicious middle section before committing to a conversion, because trailing totals and stray blank lines love to hide there. Record which delimiter, quote style and encoding the source used, so a colleague can reproduce your result. Prefer keeping numbers as text where an identifier could lose leading zeros, and be sceptical of columns whose type changes halfway down. When a document nests objects inside objects, study the outline first, then convert with the mapping in mind rather than hoping the shape survives untouched. Label the columns once and reuse the exact headings downstream, because a renamed header breaks an importer quietly and costs an afternoon to diagnose.
Compare your options
| Tool | Best for | Input | Output |
|---|---|---|---|
| CSV Delimiter Converter | Switching between comma, semicolon, tab and pipe | Delimited text or a file | The same rows under the chosen delimiter |
| CSV to JSON | Turning spreadsheet rows into JSON objects | CSV with a header row | A JSON array of objects |
| CSV Viewer | Inspecting a very large CSV | A CSV file | A searchable, virtualised table with filtered export |
When this category is NOT the answer
If the rows are really prose that happens to contain commas, converting will only formalise the mess. Clean the wording with the text tools before you decide that a table is the right shape at all.
Jobs you can do with Data tools
- Clean a messy data export before import
Repair a badly exported table in the browser: split ragged lines into columns, switch the delimiter and inspect the result before importing it anywhere.
- Move a spreadsheet into an application
Turn spreadsheet rows into JSON your code can read: convert the header row, verify the nesting and hand over a payload with no silent type changes.
Start here: a practical Data workflow
- Open the source file in CSV Viewer or JSON Tree Viewer to confirm its shape and headers.
- Convert it with CSV to JSON, JSON to CSV or XML to JSON using the mapping you need.
- Adjust the delimiter with CSV Delimiter Converter and download the finished table.
Frequently asked questions about Data tools
Is my spreadsheet uploaded when I convert it?
No. Parsing and conversion happen in your browser; the rows you open never leave your device.
Will my numbers and leading zeros be kept?
Yes. Values are treated as text rather than retyped, so codes, phone numbers and leading zeros survive the round trip.
Can these tools open a very large CSV?
CSV Viewer virtualises the table and enforces a size cap, so large files stay responsive instead of freezing the page.
How is XML mapped to JSON?
Mapping follows a documented policy for attributes, text and repeated nodes, and the pages state clearly that a perfect round trip is not guaranteed.
Categories related to Data
- Developer
Use Developer tools when you are formatting or minifying a single JSON or XML payload rather than converting between formats.
- Text
Choose Text tools when the input is loose prose that must be cleaned before it becomes structured data.
- File
Pick File tools when several exports should be bundled into one archive before delivery.
Data guides: which tool to pick
- CSV to JSON or JSON to CSV: which direction do you need?
Decide whether to convert CSV rows into JSON objects or flatten JSON back into spreadsheet rows, with examples of each.
- JSON formatter, tree viewer, or CSV: which one do you need?
Decide whether to format and validate JSON, explore it as a tree, or flatten it into spreadsheet rows.
- Read, re-delimit, or convert a CSV: which step comes first?
Choose between browsing a large CSV, changing its delimiter, turning rows into JSON, or building CSV from plain lines.
- Format XML, validate it, or convert it to JSON?
Match your XML job to the right tool: readable indentation, structural validation, or a documented JSON mapping.
- Turn lines into a comma list, CSV, or combined text?
Decide between a single separated list, real CSV columns, line-by-line concatenation, and word pairing.