TOOLS WORX

Data Format Converter

Convert structured data between popular formats for APIs, configuration files, and data exchange. Supports JSON, XML, YAML, and CSV formats.

Data Format Converter

Convert structured data between popular formats for APIs, configuration files, and data exchange. Supports JSON, XML, YAML, and CSV formats.

Drag and drop your file here

or click to browse

Maximum file size: 50MB

Why Use This Tool?

✓ Convert API responses between JSON and XML for legacy system integration - transform modern REST API JSON responses into XML format required by older SOAP-based enterprise systems, payment gateways, government APIs mandating XML, enable interoperability between microservices using different data exchange standards without writing custom parsers

Supported Formats

  • JSON - JavaScript Object Notation
  • XML - Extensible Markup Language
  • YAML - YAML Ain't Markup Language
  • CSV - Comma Separated Values

Common Questions

  • Q: What data gets lost when converting between JSON, XML, and YAML? Format-specific features that may not translate: JSON to XML: JSON arrays become numbered elements, null values may become empty tags or omitted, boolean types (true/false) become strings, type information lost (all values become strings in XML). XML to JSON: XML attributes vs elements ambiguity (how to represent John?), mixed content challenges (text plus nested elements), namespace prefixes often stripped, comments disappear. YAML to JSON: comments lost (JSON has no comment syntax), complex anchors/references flatten, date/time types become strings. JSON to YAML: generally cleanest conversion, but formatting flexibility means multiple valid outputs for same JSON. Best practice: if round-trip conversion critical (convert and convert back maintaining fidelity), test with sample data first, some converters offer options for handling ambiguous cases (XML attributes as properties vs nested objects).
  • Q: How should I handle nested hierarchical data when converting CSV to JSON or XML? CSV limitation: CSV is inherently flat (rows and columns), lacks native hierarchy unlike JSON objects or XML trees. Converting flat CSV to hierarchical format requires interpretation: (1) Column naming convention - 'user.name', 'user.email', 'address.street' column names indicate nesting (user object with name and email, address object with street), converter creates {user: {name: 'John', email: 'john@test.com'}, address: {street: '123 Main'}}. (2) Parent-child relationships - ID columns like 'user_id' linking rows requires custom logic, not automatic in most converters. (3) Array detection - repeated column names become arrays. Going opposite direction (JSON/XML to CSV): nested objects flatten into dot-notation columns (user.name, user.email), arrays create multiple rows or JSON-encoded string columns, data denormalization occurs. For complex hierarchies CSV inadequate - use JSON or XML natively instead of forcing CSV round-trip.
  • Q: Are there compatibility issues between YAML versions or JSON standards I should know about? YAML versions: YAML 1.1 vs 1.2 have subtle differences - YAML 1.1: octal numbers as 0755, YES/NO as booleans, timestamps auto-detected from strings. YAML 1.2: stricter, more JSON-compatible (YES is string not boolean), clearer type system, recommended for new projects. Most tools default to 1.1 (legacy), but 1.2 gaining adoption. JSON standards: JSON itself is stable (RFC 8259), but extensions exist - JSONC (JSON with comments, VS Code uses this), JSON5 (relaxed syntax with trailing commas, unquoted keys), standard JSON (strict, no comments). Conversion issues: YAML comments to JSON (lost), JSON numeric precision (JavaScript Number limits), YAML timestamps to JSON (become strings). Best practice: stick to standard JSON and YAML 1.2 for maximum compatibility, avoid vendor-specific extensions unless tooling explicitly supports them.
  • Q: Can I preserve comments and documentation when converting configuration files? Comment preservation by format: YAML: supports comments (# comment), widely used for documenting configs. JSON: NO comment syntax (by design), often cited as JSON weakness for configuration files. XML: supports comments (), useful for documentation. CSV: no formal comment syntax, sometimes # at line start ignored by parsers (unreliable). Conversion reality: YAML → JSON loses all comments (JSON can't represent), JSON → YAML can't create comments (source has none), XML → JSON loses comments, YAML → XML could theoretically preserve as XML comments but most converters don't. Workarounds: (1) Store comments in separate documentation file, (2) Use YAML or XML for config (both support comments), only convert to JSON when required by consuming application, (3) Some tools support JSONC (JSON with comments) but not standard. If documentation critical, choose format supporting comments (YAML preferred for human-edited configs, XML for enterprise/standards-based configs) and avoid formats lacking comment support.

Pro Tips & Best Practices

  • 💡 Validate converted data structure matches expected schema before deployment: Conversion validation steps: (1) Parse converted data in target system before using in production, (2) Check key fields exist and have correct types (IDs are numbers not strings, dates are valid), (3) Verify nested structure depth matches expectations, (4) Test with sample data covering edge cases (nulls, empty arrays, special characters), (5) Use schema validators (JSON Schema, XML XSD, YAML linters) to catch structural issues. Common post-conversion bugs: numbers converted to strings breaking calculations, null values becoming string 'null', date formats changing (ISO 8601 to epoch timestamp), boolean true becoming string 'true'. Catching these in development vs production: development = annoying, production = disaster. Automated testing: write tests that convert sample data and verify against expected output, catches regressions when converter implementation changes.
  • 💡 Use consistent indentation and formatting for human-readable config files: Format readability standards: YAML: 2-space indentation standard (4 spaces acceptable but verbose), consistent spacing around colons. JSON: 2 or 4-space indentation, avoid minified for configs (use minified only for APIs/transmission). XML: 2-space indentation, attributes on same line or multi-line for many attributes. Why formatting matters: configs are read and edited by humans (developers, ops teams), inconsistent formatting increases error risk (wrong indentation in YAML changes structure), version control diffs become unreadable with formatting changes mixed with content changes. Tools: use formatters (prettier for JSON/YAML, xmllint for XML) in git pre-commit hooks ensuring consistency, EditorConfig files enforce indentation standards across team, linters catch structural issues early. Minified vs formatted: minified for network transmission (APIs, data transfer), formatted for human interaction (configs, documentation examples).
  • 💡 Be cautious with large file conversions - test with samples first: Large file risks: memory consumption (converting 500MB JSON file can spike to 2GB+ RAM), browser tab crashes on client-side conversion, conversion time (5 minutes for large XML to JSON), network timeouts if using server-side tools. Mitigation strategies: (1) Test with small sample first (first 100 records from 100k record CSV), verify conversion logic correct before full dataset, (2) Split large files into chunks (process 10k rows at a time), (3) Use streaming converters if available (process line-by-line rather than loading entire file), (4) For very large files (>100MB) use command-line tools (jq, yq, xmlstarlet) not browser-based converters, (5) Consider if format conversion necessary (maybe target system can consume original format with adapter). Size thresholds: under 10MB = browser tools fine, 10-100MB = works but slow/careful, over 100MB = command-line tools or specialized data processing pipelines recommended.
  • 💡 Understand type coercion - numbers, booleans, nulls differ between formats: Type handling differences: JSON: numbers, strings, booleans (true/false), null, arrays, objects - strict types. XML: everything is text (no native number type), requires schema (XSD) for type information, '123' is string not number unless schema defines. YAML: rich types (dates, timestamps, numbers, booleans, null), type inference (123 is number, '123' is string, yes is boolean). CSV: all values are strings, applications interpret (Excel auto-converts, Python Pandas uses type inference). Conversion impacts: YAML → JSON: dates become strings ('2024-01-15T10:00:00Z'), nulls preserved as JSON null. XML → JSON: '123' might stay string or convert to number depending on converter, 'true' might be string or boolean. CSV → JSON: need to specify which columns are numbers vs strings. Best practice: be explicit (quote strings in YAML, use type hints if converter supports, validate output types), test edge cases (0, negative numbers, very large numbers, empty strings vs nulls).
  • 💡 Keep original files as backup before bulk format migration: Migration safety: converting 200 YAML config files to JSON for new framework is high-risk operation - one bug affects all files, manual verification of 200 files impractical. Backup workflow: (1) Commit all originals to version control before conversion, (2) Convert all files to new format in separate directory, (3) Test application with converted files in staging, (4) Compare samples manually for sanity check, (5) Keep originals for 30-60 days after production deployment, (6) Only delete originals after confirming new format works in production. Rollback plan: if converted configs cause production issues, revert to originals immediately while debugging conversion. Horror story: team converted all XMLs to JSON, deleted originals, discovered converter had bug with attribute handling weeks later, spent days manually recreating XMLs from JSON because structure information was lost. Disk space is cheap, re-creating files manually is expensive. Archive originals, generate conversions, verify thoroughly, only then consider cleanup.

When to Use This Tool

  • API Integration & Data Exchange: Convert REST API JSON responses to XML for legacy SOAP service integration, transform XML SOAP responses to JSON for modern frontend consumption, adapt third-party API data formats to match internal system requirements, migrate between API providers requiring different data serialization formats maintaining business logic unchanged
  • Configuration Management: Convert Docker Compose YAML to JSON for programmatic configuration generation in deployment pipelines, migrate Kubernetes manifests between YAML and JSON for different tooling requirements, transform application configs when switching frameworks from XML-based Spring to JSON-based Node.js applications, standardize infrastructure-as-code definitions across multiple configuration formats for unified management
  • Data Migration & ETL: Convert CSV database exports to JSON for import into MongoDB, NoSQL databases expecting document format, transform XML exports from enterprise systems to JSON for data warehouse loading and analytics processing, migrate legacy XML data stores to modern JSON-based databases during system modernization, prepare flat CSV files for import into hierarchical data structures with proper nesting
  • DevOps & Infrastructure: Convert Ansible YAML playbooks to JSON for programmatic manipulation in automation scripts, transform Terraform JSON to YAML for improved readability in infrastructure repository, migrate CloudFormation templates between JSON and YAML based on team preferences and tooling support, convert CI/CD pipeline configs between formats when switching from Jenkins XML to GitLab YAML
  • Cross-Language Development: Convert YAML configuration preferred by Python/Ruby developers to JSON for JavaScript/TypeScript consumption, transform Java application XML configs to YAML for containerized microservices deployment, ensure configuration portability between backend services written in different languages with different format preferences, maintain single source of truth converting to required formats automatically
  • Documentation & Examples: Convert API documentation examples between JSON and XML showing equivalent requests in different formats, transform tutorial code samples from YAML to JSON for broader language compatibility, prepare data format examples in multiple serialization formats for comprehensive technical documentation, generate formatted configuration templates from JSON schemas for distribution across teams

Related Tools

Quick Tips & Navigation