Free Data Format Converters: JSON, CSV, XML, YAML and More

Each format was designed to solve a specific problem. Understanding those problems helps you pick the right format — and know when you need to convert.

JSON: The Lingua Franca of APIs

JSON (JavaScript Object Notation) became the default data interchange format for a reason. It is lightweight, widely supported, and maps naturally to data structures in most programming languages. Every modern REST API returns JSON. Every JavaScript developer thinks in JSON.

JSON handles nested, hierarchical data well. An object can contain arrays, which can contain more objects, which can contain more arrays. This makes it ideal for representing complex, real-world data — a user profile with multiple addresses, each containing a street, city, and postal code.

Where JSON falls short: it does not support comments, which makes it awkward for configuration files where you want to explain why a setting has a particular value. It also has no native concept of dates, so you end up with strings like "2026-03-27T10:30:00Z" that need to be parsed separately.

Use the JSON Formatter to validate and pretty-print JSON before converting it to other formats.

CSV: The Universal Spreadsheet Format

CSV (Comma-Separated Values) is the oldest and simplest format in this list. It is just rows and columns, separated by commas, with an optional header row. Every spreadsheet application, database tool, and data analysis library can read CSV.

CSV excels at flat, tabular data — the kind of data you would put in a spreadsheet or a database table. Customer lists, transaction logs, sensor readings, survey responses. If your data is a rectangle of rows and columns, CSV is probably the most portable way to store it.

CSV struggles with hierarchical data. If a customer has multiple orders, and each order has multiple items, you cannot represent that cleanly in CSV without either flattening it (duplicating the customer data on every row) or splitting it into multiple files (customers.csv, orders.csv, items.csv).

Use the CSV Viewer to inspect and explore CSV files in a spreadsheet-like interface right in your browser.

XML: The Enterprise Workhorse

XML (Extensible Markup Language) dominated data interchange in the 2000s. SOAP APIs, RSS feeds, Office documents, SVG graphics, Android layouts — all XML. It is verbose, but that verbosity comes with power. XML supports namespaces, schemas, validation, attributes, and comments. You can define exactly what a valid document looks like using XSD, and validators will tell you if a document conforms.

Many enterprise systems, government APIs, and financial data feeds still use XML. If you work with SOAP services, HL7 healthcare data, or legal document standards, you work with XML daily.

The main complaints about XML are verbosity and readability. A simple key-value pair that is "name": "Alice" in JSON becomes <name>Alice</name> in XML. For large datasets, this overhead adds up in file size and parsing time.

YAML: The Config File Champion

YAML (YAML Ain't Markup Language) was designed specifically to be human-readable. It uses indentation instead of brackets, supports comments, and looks almost like plain English. This made it the natural choice for configuration files — Docker Compose, Kubernetes manifests, GitHub Actions workflows, Ansible playbooks, and countless other DevOps tools use YAML.

YAML is a superset of JSON, which means any valid JSON is also valid YAML. But YAML adds features that JSON lacks: comments (essential for config files), multi-line strings, anchors and aliases for reusing values, and a more relaxed syntax that does not require quotes around most strings.

The downside of YAML is that indentation is significant, and indentation errors are silent and subtle. A misplaced space can change the structure of your data without producing a syntax error. This has caused more than a few production outages.

TOML: The Readable Config Alternative

TOML (Tom's Obvious, Minimal Language) was created by Tom Preston-Werner (GitHub cofounder) as a configuration file format that is even simpler than YAML. It looks like an INI file with clearly defined types. Rust's Cargo uses it for Cargo.toml, Python uses it for pyproject.toml, and it is gaining adoption in the Go ecosystem.

TOML handles flat and moderately nested configuration well but becomes unwieldy for deeply nested data. That is by design — if your config is deeply nested, maybe you should rethink your config structure.

Common Conversion Scenarios

Now that you understand what each format does well, here are the real-world scenarios where you need to convert between them.

JSON to CSV: Making API Data Spreadsheet-Ready

This is probably the most common conversion. You call an API, get back a JSON array of objects, and need to analyze the data in Excel, Google Sheets, or a business intelligence tool. Or you need to send the data to someone who does not have a JSON viewer but absolutely knows how to use a spreadsheet.

Typical use case: You query a user analytics API and get back JSON with user IDs, signup dates, plan types, and activity counts. Your marketing team needs this in a spreadsheet to build a report. Convert the JSON array to CSV, hand them the file, done.

Use JSON to CSV to flatten JSON arrays into clean CSV tables instantly.

CSV to JSON: Importing Spreadsheet Data into Applications

The reverse is equally common. Someone gives you a spreadsheet of products, contacts, or configuration data, and you need to load it into a web application, send it to an API, or store it in a document database.

Typical use case: Your operations team maintains a product catalog in Google Sheets. Your e-commerce backend expects JSON. Export the sheet as CSV, convert it to JSON, and you have an array of product objects ready for import.

Use CSV to JSON to turn any CSV file into a structured JSON array.

JSON to XML: Bridging Modern and Legacy Systems

When a modern microservice that speaks JSON needs to communicate with a legacy SOAP service, ERP system, or government API that expects XML, you need this conversion. It is also common when generating RSS feeds, creating SVG graphics programmatically, or producing documents in XML-based standards.

Typical use case: Your Node.js backend receives order data as JSON from your frontend. The fulfillment system is a SOAP service from 2008 that only accepts XML. You convert the JSON order to XML, wrap it in a SOAP envelope, and send it off.

Use JSON to XML to generate well-formed XML from any JSON structure.

XML to JSON: Modernizing Data Pipelines

If you are migrating away from XML-based systems, or you need to consume data from XML APIs in a modern JavaScript application, this is your conversion. Parsing XML in JavaScript is painful compared to JSON.parse().

Typical use case: You are integrating with a banking API that returns account statements as XML. Your React dashboard expects JSON. Convert the XML response to JSON, and your frontend components can consume the data directly.

Use XML to JSON to parse XML into clean, usable JSON objects.

JSON to YAML: Creating Configuration Files

When you are setting up a new service and need to write a Docker Compose file, a Kubernetes manifest, or a CI/CD pipeline, you often start with JSON data — maybe from an existing config or an API response — and need to express it as YAML.

Typical use case: You have a working deployment as a JSON config object. You need to create a Kubernetes deployment manifest in YAML. Convert the JSON to YAML, adjust the Kubernetes-specific fields, and apply it.

Use JSON to YAML to convert any JSON document to clean, readable YAML.

YAML to JSON: Programmatic Config Processing

YAML is great for humans but annoying for machines. When you need to programmatically read, validate, or transform configuration files, converting YAML to JSON first often simplifies the code. Most programming languages have better JSON support than YAML support.

Typical use case: You need to write a script that validates your Kubernetes manifests, checks for required labels, and reports missing fields. Convert the YAML to JSON, and you can use standard JSON path queries and schema validation.

Use YAML to JSON to convert YAML configs into machine-friendly JSON.

Data Migration Strategies

When you are migrating data between systems, format conversion is often the first step. Here are some patterns that work well.

The CSV Staging Pattern

Many migrations follow this flow: export from the old system as CSV (because everything can export CSV), transform the data (rename columns, clean values, merge fields), then convert to the target format (JSON for an API, XML for an enterprise system, YAML for configuration).

CSV works as a staging format because it is the lowest common denominator. Any system can produce it, any tool can read it, and any spreadsheet can edit it. This lets non-technical team members review and clean data before it reaches the target system.

The JSON Hub Pattern

If you are moving data between multiple systems that each use different formats, pick JSON as your canonical intermediate format. Convert everything to JSON first, validate and transform it there, then convert to whatever the target system needs. JSON's broad tooling support and simple structure make it an ideal hub format.

Handling Nested Data in Flat Formats

The hardest part of converting between hierarchical formats (JSON, XML, YAML) and flat formats (CSV, TSV) is dealing with nesting. A JSON object with nested arrays does not map cleanly to CSV rows.

Common strategies include:

• Flattening with dot notation: address.city becomes a column header, and nested values become individual cells.
• Separate tables: Split the data into multiple CSV files, one per entity type, linked by IDs (like a relational database).
• JSON-in-cells: Keep complex nested values as JSON strings within a CSV cell. This is ugly but practical when the nested data varies per row.

API Response Transformation

If you work with APIs, you transform data formats constantly. Here are scenarios you will encounter.

Aggregating multiple APIs: You call three different services — one returns JSON, one returns XML, one returns CSV. You need to combine the results. Convert everything to JSON, merge the arrays or objects, and return a unified response.

Format negotiation: Your API needs to support multiple response formats based on an Accept header. Store data internally as JSON, then convert on the fly to XML or CSV when clients request it.

Webhook transformation: An incoming webhook sends XML, but your processing pipeline expects JSON. A lightweight conversion step at the edge handles the translation without changing your entire pipeline.

Tips for Clean Conversions

A few things to watch out for when converting between formats.

Character encoding matters. Make sure your source and target agree on UTF-8. CSV files from Excel are sometimes encoded as Windows-1252 or Latin-1, which breaks special characters when treated as UTF-8.

Number precision varies. JSON uses IEEE 754 floating-point numbers, which means very large integers (above 2 to the power of 53) lose precision. If your CSV contains 18-digit account numbers, they might get rounded when converted to JSON numbers. Keep them as strings.

Empty values are ambiguous. An empty CSV cell could mean null, empty string, zero, or "not provided." JSON distinguishes between null, "", and 0, but CSV does not. Decide on your convention before converting.

Date formats are inconsistent. CSV has no standard date format. JSON has no date type at all. XML has xs:dateTime. YAML has its own date parsing (which can cause surprises — 2026-03-27 is automatically parsed as a date, not a string). Be explicit about date formats in every conversion.

Column order is not guaranteed. JSON objects are technically unordered. When converting JSON to CSV, the column order might differ between runs or tools. If column order matters (and it usually does for downstream consumers), verify it after conversion.

All Processing Happens in Your Browser

Every conversion tool linked in this post runs entirely in your browser. Your data never leaves your machine. There is no upload to a server, no storage, no logging. This matters when you are working with customer data, financial records, API keys, or any other sensitive information.

Paste your data in, get the converted result out. No account required, no rate limits, no file size restrictions beyond what your browser can handle.

Quick Reference: Which Converter Do You Need

Data format conversion is one of those everyday tasks that should take seconds, not minutes. Bookmark the converters you use most, and stop writing throwaway scripts for something that should be a copy-paste operation.