Use JSON and TSV without hiding data-shape changes
This tool helps you turn JSON records into tab-separated rows, or rebuild JSON records from TSV data. It is intended for database pastes, command-line output, data notebooks, and tables whose values often contain commas. Paste a small representative sample first, choose the direction, and compare the visible result with the source before you use a larger file. The page never sends the working data to NexaCurrent, so ordinary conversion stays inside the current browser tab.
Try an array of multilingual inventory records, including commas inside product names. The result area reports the row count and shows the output column paths. Check that nested values and identifiers appear in the intended columns before using the result. If the two formats describe data differently, the page explains the exact compromise instead of simply announcing that the conversion succeeded. That review step matters whenever the result will feed another application.
Choose the direction and column-path behavior
Use the first conversion button for the direction named on its label and the second button for the reverse direction. Keep the path-preserving choice for nested input. A heading such as /address/city identifies where a value came from and avoids collisions between similarly named fields. Readable dot-separated headings such as address.city are easier to scan, but they do not retain enough information to rebuild nesting automatically.
For table downloads, ordinary UTF-8 works in modern editors and data tools. The UTF-8 with BOM option adds a small marker to the downloaded copy for spreadsheet programs that otherwise guess the wrong character encoding. The marker is not added to the visible result. Copy the visible text when you want clean clipboard data, or use the named download when you need a file.
Read the mapping preview before using the result
The mapping preview lists the columns produced or read by the conversion. Check that important identifiers, names, dates, amounts, and nested values are present. Tabs and line breaks inside values are quoted, while nested objects use visible path labels. A warning is therefore useful information, not a generic error. It tells you where a reverse conversion may differ from the original even though the output remains syntactically valid.
Empty table cells remain empty strings on conversion back to JSON or YAML because guessing null, zero, false, or a missing value would silently change meaning. Numbers and booleans also remain strings after a table round trip. If the destination has a schema, apply its type rules after conversion and validate a few known records. Keep the original source until that comparison is complete.
Handle quoted fields, JSON arrays, and value types
CSV and TSV fields may contain their delimiter, quotation marks, or line breaks. The converter reads doubled quotation marks and writes quotes only where required, so a value such as “Café, North” remains one field. Nested JSON objects become visible path columns, while a nested array is stored as compact JSON text inside one cell and receives a review warning.
A table works best when every JSON array item describes the same kind of record. Numbers, booleans, and null values cannot retain their JSON types through a text table without a separate schema. The converter therefore shows the type change and leaves reverse-converted table values as strings instead of making unsafe guesses.
Limits, errors, privacy, and a practical final check
Empty input, malformed JSON, unsafe or colliding column paths, excessive nesting, inconsistent row widths, duplicate headings, unclosed quoted fields, and oversized data stop with a visible message. The page limits input, rows, columns, and total cells so an accidental paste cannot freeze the tab. For very large datasets, use a streaming desktop or command-line workflow instead.
Reset clears the input, result, mapping notice, warnings, and download from this page. Before sharing a converted file, open it in the intended destination, count the records, inspect several rows with punctuation or non-English text, and confirm the fields used as identifiers. A successful conversion confirms the rules shown here; it does not prove that a private schema, business rule, or third-party importer will accept every value.