Start with Clean Headers
Column headers are the foundation of any data conversion. Use short, descriptive names without spaces or special characters. 'first_name' or 'firstName' converts cleanly to JSON keys and XML element names; 'First Name (required)' does not.
Every row should have the same columns in the same order. Missing columns in some rows create sparse data that is harder to validate and import.
Consistent Data Types
Keep each column to one data type. A 'date' column should contain only dates, not mixed dates and text notes. An 'amount' column should contain only numbers, not values like 'N/A' or 'pending'. Mixed types cause import errors in databases and misformatted output in converters.
- Dates: Use ISO 8601 format (2024-03-15) for universal compatibility.
- Numbers: No currency symbols or thousand separators in data columns.
- IDs: Format as text to preserve leading zeros (e.g., '00123' not 123).
- Empty values: Use empty cells, not 'N/A', 'null', or '-' unless those are meaningful.
- Booleans: Stick to true/false or yes/no consistently within a column.
Preparing Data for Conversion
Before running any converter, review your source data in the Online CSV Editor or Online XLSX Editor. Remove blank rows, fix header names, and check that special characters display correctly. A few minutes of cleanup prevents most conversion errors.
If your data came from a web copy-paste or PDF extraction, expect extra whitespace, merged cells, and stray characters. Clean these in the editor before converting to JSON, XML, SQL, or other structured formats.
Validating Output
Always preview converted output before downloading. Check row counts (did you lose or gain rows?), column names (are they correct?), and sample values (do dates, numbers, and text look right?). For SQL output, scan for unescaped quotes. For JSON, verify the array structure. For XML, check element names.
File Size and Performance
Convert CSV processes files entirely in your browser. Files under 1 MB convert instantly; files approaching 5 MB may take a few seconds. For very large datasets, split the source into smaller files, convert each, and combine the results in the editor or with a script.