Instantly convert between structured data formats, flatten JSON, detect schemas, and generate API models or SQL structures in your browser.
Data exists in many shapes and sizes. Whether you are migrating a database, connecting to a new API, or analyzing logs, you frequently need to transform data from one format to another. This tool simplifies those tedious tasks by letting you convert between JSON, CSV, YAML, XML, and SQL instantly, right in your browser without any server processing.
Instead of writing custom scripts for one-off data migrations, you can paste your raw data here. The Data Format Transformer will instantly detect the structure and convert it to your desired output. It handles large datasets efficiently and ensures your data never leaves your computer, guaranteeing total privacy.
JSON (JavaScript Object Notation) is a lightweight, text-based data format used extensively in web development and APIs. It represents structured data using key-value pairs and arrays, making it both human-readable and easy for machines to parse. JSON is the de facto standard for data interchange between frontend and backend systems, mobile apps, and microservices.
CSV (Comma-Separated Values) is a simple, flat-file format where each row represents a record and columns are separated by commas, semicolons, or tabs. It is widely used in spreadsheets, databases, and data analysis tools. CSV is the most common format for importing and exporting tabular data from applications like Excel, Google Sheets, and SQL databases.
YAML (YAML Ain't Markup Language) is a human-friendly data serialization format commonly used for configuration files, CI/CD pipelines (like GitHub Actions, Docker Compose), and Kubernetes manifests. Its indentation-based syntax makes it very readable, though its sensitivity to whitespace can sometimes lead to subtle errors.
XML (Extensible Markup Language) is a markup language that uses custom tags to define the structure of documents. Although it has been largely replaced by JSON in modern APIs, XML remains critical in enterprise environments, SOAP web services, RSS feeds, SVG graphics, and legacy integrations. It supports powerful features like schemas (XSD) and transformations (XSLT).
JSON flattening is the process of taking a deeply nested JSON object and compressing it into a single, flat structure using dot or underscore notation (e.g., transforming user.address.city from {"user":{"address":{"city":"Rome"}}}). This is essential when you need to export complex API responses into a CSV spreadsheet where nested objects aren't supported, or when preparing data for flat-file databases and analytics tools.
When working with unknown or undocumented APIs, understanding the data structure can consume hours. Our Detect Schema feature instantly analyzes your JSON payload and infers the data types (strings, numbers, booleans, arrays, nested objects). This is invaluable when reverse-engineering third-party APIs, planning database schemas, or writing data validation rules.
The Generate Models feature takes schema detection further by instantly writing the boilerplate code for your backend. Whether you need TypeScript Interfaces, Python Pydantic models, Java Classes, or Go Structs, the tool generates production-ready code you can paste directly into your project. This eliminates typos, ensures type consistency, and can save hours of manual work.
Every conversion, detection, and model generation runs 100% client-side in your browser. Your data is never uploaded to any server, never logged, and never stored. This tool is safe for proprietary, confidential, and sensitive datasets.
{ "address": { "city": "Rome" } } becomes { "address.city": "Rome" }. It makes hierarchical data compatible with flat formats like CSV. Take this simple JSON array representing three products:
[{"id":1,"name":"Widget","price":9.99},{"id":2,"name":"Gadget","price":24.99},{"id":3,"name":"Doohickey","price":4.49}]
The converter flattens the array into rows, uses the JSON keys as CSV column headers, and outputs three data rows. Nested objects would be flattened to dot-notation keys (e.g., "address.city") before CSV conversion. The same input converted to YAML produces a clean, human-readable block. Converted to SQL, it generates a CREATE TABLE statement with inferred column types followed by INSERT statements.
Developers working with REST APIs frequently receive data in JSON and need to transform it into CSV for spreadsheet analysis, YAML for config files, or SQL for database ingestion. This tool eliminates the need to write one-off conversion scripts.
Moving data between systems often requires format translation. Converting a CSV export from MySQL into a YAML fixture for a Rails test suite, or transforming a JSON API payload into SQL INSERT statements, are routine migration tasks this tool handles in seconds.
DevOps engineers and platform teams work with configuration in JSON (package.json, tsconfig), YAML (Docker Compose, GitHub Actions, Kubernetes manifests), and sometimes XML (Maven, Spring). Being able to convert between formats speeds up onboarding and format standardization.
Data analysts and scientists often receive raw data in JSON from APIs or logs. Converting it to CSV with a single click makes it immediately importable into Excel, Google Sheets, Pandas, or R — no scripting required.
Many enterprise and government systems still consume XML. Converting modern JSON responses into XML-compatible payloads, or the reverse, is a common integration task that this tool performs without needing a dedicated middleware layer.
Every conversion runs entirely in your browser using a JavaScript pipeline that parses the input format into an intermediate representation, then serializes it into the target format.
The input is parsed according to its format: JSON.parse() for JSON, PapaParse for CSV, js-yaml for YAML, and a custom DOM parser for XML. The result is a JavaScript object tree that serves as the canonical intermediate representation.
The intermediate object is serialized into the target format using the corresponding serializer. Advanced options (Pretty Print, Sort Keys, Remove Nulls, Minify) are applied at this stage before the final string is generated.
Flattening traverses the object tree recursively and collapses nested keys into a single level using dot notation. This is essential for CSV conversion because flat files cannot represent nested structures.
The schema detector analyzes the intermediate object tree and infers data types for every key: string, number, boolean, array, object, or null. The result is a JSON Schema-compatible document that describes the structure of the input.
Any professional who works with structured data will find a use case here.
A few habits make format conversion faster and the output cleaner.
Modern software systems rarely speak a single data language. A web API returns JSON. A finance system exports CSV. A DevOps pipeline expects YAML. A legacy integration requires XML. A data warehouse ingests SQL. Translating between these formats manually is slow, error-prone, and a poor use of engineering time.
Every conversion, schema detection, model generation, and SQL output runs entirely inside your browser. No data is sent to any server at any point. The tool uses PapaParse for CSV, js-yaml for YAML, and native browser APIs for JSON and XML — all loaded once and then working offline. Proprietary schemas, confidential API payloads, and internal database structures can be safely processed without any data leaving your device.
JavaScript Object Notation. Lightweight key-value pairs and arrays. The standard data interchange format for web APIs and modern applications. Human-readable and machine-parseable.
Comma-Separated Values. A flat, row-based format where each line is a record and columns are delimited by commas (or semicolons/tabs). Native to spreadsheets and database exports.
YAML Ain't Markup Language. An indentation-based format optimized for human readability. The standard for CI/CD configuration files (GitHub Actions, Docker Compose, Kubernetes).
Extensible Markup Language. A tag-based hierarchical format. Verbose but self-describing. Still dominant in enterprise systems, SOAP APIs, RSS feeds, SVG, and Office document formats.
Structured Query Language. Not a data format per se, but a language for defining (CREATE TABLE) and populating (INSERT INTO) relational databases. Generating SQL from structured data bridges the gap between flat files and relational systems.
JSON was formally specified by Douglas Crockford in 2001 as a lighter alternative to XML for data interchange between JavaScript applications and servers. Crockford later admitted that he "discovered" JSON rather than invented it — the format was already a natural consequence of JavaScript's object literal syntax. By 2013, JSON had officially overtaken XML as the most common API data format on the web. Today, the ECMA-404 standard defines the JSON specification, and virtually every programming language ships a built-in JSON parser.
The Data Format Converter is the all-in-one solution for developers, analysts, and engineers who work with structured data across different systems and tools. Convert between JSON, CSV, YAML, XML, and SQL in seconds, flatten nested structures, detect schemas, generate typed code models, and produce SQL statements — all without writing a single line of code and without sending any data to a server. Paste your data and transform it instantly.