Data tools family
Online Data Formatting and Conversion Guide
Format, validate, parse, clean, and convert structured data while preserving types, delimiters, escaping, encoding, and a verifiable source copy.
What this page helps you do
Data operations answer different questions. Formatting changes presentation, such as indentation or line breaks. Minification removes optional presentation characters. Validation checks data against syntax or a schema. Parsing converts text into an internal structure. Conversion maps one structure or format to another. A successful format operation does not prove that the data is complete, semantically correct, or safe to import.
The published grid below lists the resources available in this family. This page provides a format-selection and quality-control framework for JSON, CSV, XML, YAML, GraphQL text, and related structures. Before using any tool, identify the source specification, target specification, encoding, field types, null rules, date and number conventions, and whether order or duplicate keys matter.
Lossless conversion is not always possible. CSV has no universal schema or native nested-object model. JSON distinguishes numbers, strings, booleans, null, arrays, and objects but does not define comments or a date type. XML can carry attributes, namespaces, mixed content, and order that may not map cleanly to a flat table. YAML implementations can differ in supported versions and type resolution.
Published resources in this family
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Browse all ToolsChoose the right approach
Format or minify
Choose when the structure is already valid and the goal is readability or compact transport. Formatting should not be confused with repair.
Validate or parse
Choose when you need a syntax result, schema result, or structured representation. Record the standard and validator version.
Convert or flatten
Choose when the target has different capabilities, then document type coercion, nesting, arrays, nulls, duplicate fields, and ordering.
Reference table
| Format | Strength | Conversion risk |
|---|---|---|
| JSON | Typed values, arrays, and nested objects with a widely used syntax | Duplicate member handling, large numbers, and dates can vary by implementation. |
| CSV | Simple tabular exchange and broad spreadsheet support | Delimiter, quoting, encoding, line breaks, and schema are external conventions. |
| XML | Namespaces, attributes, mixed content, validation systems, and ordered trees | Flattening can lose hierarchy, attributes, namespace meaning, or order. |
| YAML | Human-oriented configuration with mappings and sequences | Version, indentation, anchors, tags, and implicit typing require care. |
| GraphQL document | Readable operations, fragments, and schema language text | Formatting does not execute, authorize, or validate against a live schema. |
| Plain text table | Easy human review for small datasets | Alignment is not a reliable machine-readable delimiter. |
| Minified data | Reduced whitespace for transport or storage | Debugging becomes harder, and minification does not reduce binary payloads equally. |
Worked example: CSV quoting and JSON types
Consider a CSV row with the fields name, note, and amount. The note value is: She said, “review it”. Because the value contains a comma and quotation marks, a conventional comma-delimited representation must quote the field and escape embedded quotation marks according to the chosen CSV convention.
A naive split on commas would create too many columns. A CSV-aware parser should read the delimiter, quote character, escape rule, line-ending convention, and encoding. Before import, confirm whether the amount “0012” is an identifier-like string or the numeric value 12; a spreadsheet may remove leading zeros automatically.
When converting to JSON, choose types deliberately. The value "0012" as a string preserves the leading zeros, while 12 as a number does not. Empty text, a missing field, and JSON null are three distinct states and should not be collapsed unless the target schema says they are equivalent.
After conversion, compare record counts, field counts, null counts, unique identifiers, date ranges, numeric totals, and a sample of escaped multilingual text. Save an error report for rows that could not be mapped instead of silently dropping them.
Data-conversion checklist
Define both schemas
List fields, types, required values, nesting, order, keys, dates, numbers, nulls, and encoding for source and target.
Use a real parser
Avoid ad hoc splitting for CSV, XML, JSON, YAML, or GraphQL when escaping and structure matter.
Reconcile the output
Compare counts, identifiers, totals, nulls, duplicates, checksums, and representative records.
Protect the data
Work on copies, avoid sensitive uploads, set file-size expectations, and retain logs for rejected or changed records.
Assumptions, limitations, and review
Browser memory use can exceed the file size because text, parsed objects, copies, and rendered output may coexist. Large input can freeze a tab, trigger an out-of-memory error, or produce an incomplete download. Use streaming or server-side tools designed for large datasets when appropriate.
A formatter cannot verify business meaning. A syntactically valid date can still be in the wrong timezone, a valid number can use the wrong unit, and a valid identifier can refer to the wrong entity.
Do not assume a conversion is lossless. Review namespaces, attributes, comments, precision, large integers, duplicate keys, ordering, formulas, leading zeros, line breaks, and encoding. Keep the source and a documented mapping.
Related TestsAndTools pages
External reference resources
These links support further verification and learning. External sites have their own content, privacy, and accessibility practices.
RFC 8259: The JavaScript Object Notation Data Interchange Format
The IETF-standard reference for JSON grammar, interoperability considerations, strings, numbers, and objects.
W3C: XML 1.0, Fifth Edition
The W3C recommendation defining XML documents, well-formedness, characters, and processing requirements.
Frequently asked questions
Is formatted JSON automatically valid?
Only if the formatter successfully parses valid JSON. Pretty appearance does not establish schema validity or business correctness.
Why does CSV import differently in two programs?
CSV behaviour depends on delimiter, quoting, encoding, locale, line endings, automatic typing, and program-specific import settings.
Can XML always be converted to CSV?
Not without choices. Nested elements, repeated groups, attributes, namespaces, mixed content, and order may require multiple tables or a custom mapping.
Does a GraphQL formatter validate an operation?
Formatting can check or normalize syntax in some tools, but schema validation, authorization, variables, and execution require the relevant schema and environment.
How can I detect data loss after conversion?
Reconcile counts, keys, types, nulls, totals, precision, ordering, and sampled records, and log every rejected or transformed value.