The ideal format is an array of objects, where each object represents a row and each key represents a column. For example: [{"name":"John","age":30},{"name":"Jane","age":25}]. A single object is also supported and will be converted to a one-row spreadsheet. Nested objects can be automatically flattened using dot-notation keys (e.g., address.city). If your JSON is a primitive value or an array of primitives, it will be converted to a single-column sheet.

When the "Flatten nested objects" toggle is enabled, nested objects are expanded into separate columns using dot notation. For instance, {"user":{"name":"Alice","address":{"city":"Paris"}}} becomes columns: user.name and user.address.city. This makes nested data fully visible in a flat spreadsheet format. Arrays within objects are joined into comma-separated strings for clean display.

This tool runs entirely in your browser using the SheetJS library. It can comfortably handle up to 50,000 rows and 100+ columns on modern devices. For very large datasets (100,000+ rows), performance may vary based on your device's memory. Excel itself has a limit of 1,048,576 rows and 16,384 columns per sheet. The preview displays up to 500 rows for optimal performance, but the exported .xlsx file includes all your data.

The converter preserves strings, numbers, booleans, and null values. Numbers remain as numeric cells in Excel (allowing formulas and calculations). Boolean values are exported as TRUE/FALSE. Null values appear as empty cells. Date strings (like "2024-01-15") are exported as text; you can format them as dates in Excel after opening the file using Excel's built-in date formatting tools.

This tool creates a single-sheet .xlsx file per conversion. If you need multiple sheets in one workbook, you can convert each JSON file separately, then combine the sheets using Excel's "Move or Copy Sheet" feature, or use a more advanced tool like Python's openpyxl or xlsxwriter libraries. For most use cases, a single well-structured sheet is sufficient.

Your data never leaves your browser. All JSON parsing, table preview generation, and Excel file creation happens entirely on your device using client-side JavaScript. No data is uploaded to any server. This means your sensitive information remains private and secure. You can even use this tool offline after the page has loaded.

Common use cases include: exporting API response data for reporting, converting database query results (JSON format) into spreadsheets for analysis, transforming configuration files into editable Excel formats, creating data inventories from JSON logs, migrating NoSQL data to spreadsheet-compatible formats, and preparing machine-readable JSON data for non-technical stakeholders who prefer Excel. It's especially useful for data analysts, developers, and project managers who need to bridge structured data and spreadsheet tools.

XLSX offers several advantages over CSV: it supports multiple sheets, preserves data types (numbers stay numeric), handles special characters and Unicode without encoding issues, supports cell formatting and styling, and is the native format for Microsoft Excel and Google Sheets. CSV files are plain text and may lose number formatting, struggle with commas in data, and lack multi-sheet support. XLSX is the professional choice for data exchange.