Almost every database import tool - from psql \copy to BigQuery's load jobs to a simple ORM seed script - expects CSV, not XLSX. Excel's binary-adjacent format is great for humans working in a spreadsheet, but it's not something most database tooling parses directly. Converting to CSV first is the standard bridge between "data in a spreadsheet" and "data in a table."
Why databases prefer CSV over XLSX
XLSX is technically a ZIP archive of XML files describing sheets, styles, formulas, and metadata - a rich format built for spreadsheet applications, not database engines. CSV, by contrast, is about as simple as tabular data gets: one line per row, values separated by commas. That simplicity is exactly what makes it universally supported - every major database's bulk-import tool, every ETL pipeline, and every scripting language can read CSV with zero special libraries.
Importing XLSX directly would require the database tool to understand Excel's internal format, parse out styling and formulas, and decide what to do with multiple sheets - complexity that CSV sidesteps entirely by only ever representing one flat table.
What gets lost (and preserved) in the conversion
Converting XLSX to CSV is lossless for the data itself - every cell value carries over - but it does discard everything CSV has no concept of: cell formatting (colors, bold, borders), formulas (converted to their last calculated value, not the formula itself), multiple sheets (only one sheet exports per CSV file), and charts or embedded objects. For a database import, this is exactly the right trade-off: databases store values, not formatting, so nothing meaningful is actually lost for that use case.
Step-by-step: XLSX to CSV in your browser
1. Confirm your data is on the first sheet (or the sheet you intend to import), since a single conversion pass exports one sheet at a time.
2. Drop the file into the converter. Using the XLSX to CSV tool, your file is parsed locally using SheetJS, entirely in the browser - nothing is uploaded.
3. Download the CSV and inspect the first few rows before importing, especially checking that headers ended up in row one and that no stray blank rows were included.
Preparing CSV for PostgreSQL, MySQL, and BigQuery
Most databases are picky about a few specific things in CSV: consistent column counts across every row, a clear decision about whether the first row is a header (most bulk-load commands have a flag for this, like PostgreSQL's COPY ... WITH (FORMAT csv, HEADER)), and consistent date formatting, since spreadsheet date formats often don't match a database column's expected format and need reformatting before import.
BigQuery in particular auto-detects schema from CSV headers when using its load UI or bq load --autodetect, which makes a clean, well-headered CSV export from XLSX especially convenient - the schema step becomes nearly automatic.
Handling large spreadsheets and multiple sheets
If your workbook has multiple sheets you need imported as separate tables, you'll need to convert each sheet individually - export, switch the active sheet in your source file, export again. For very large spreadsheets (tens of thousands of rows), browser-based conversion still works but may take a moment longer since parsing happens using your device's own memory rather than a server's.
If your end goal is actually an API or application rather than a relational database, it's often more useful to skip CSV entirely and convert straight to XLSX to JSON, which produces the same row-per-object structure without an intermediate CSV step.
Encoding and delimiter checks before importing
Two CSV details cause most failed imports, and both are worth checking before you load anything. The first is character encoding: spreadsheet exports are usually UTF-8, but CSV files produced by older Windows tools can be saved in Windows-1252 or Latin-1, which silently garbles accented characters like é or ñ when a database assumes UTF-8. Save (or convert) the CSV as UTF-8 first, and the import will match. The second is the delimiter: some European Excel locales use a semicolon as the list separator, producing files that look like CSV but break comma-based parsers. The CSV you download from a converter uses commas, but if a third-party file looks wrong, check whether it is actually semicolon-delimited before blaming the importer.
A pre-import checklist for clean database loads
Before you run that COPY or load job, walk through a short checklist. Confirm every row has the same number of columns - one bad row with a stray comma is the classic way a load fails halfway through. Decide whether the first row is a header and tell the importer explicitly, using the flag your tool provides rather than relying on auto-detection. Check dates: spreadsheets often display dates in a locale-specific format, but databases generally want ISO 8601 (YYYY-MM-DD), so reformat date columns in the spreadsheet before converting. Decide how empty cells should be treated - most databases distinguish NULL from an empty string, and your import configuration needs to say which one you mean. And finally, trim stray whitespace around cell values, since " name" and name are different values to a database.
These checks take minutes and turn what is often a debugging session into a single successful load.
Using CSV in ETL and data pipelines
Beyond one-off database imports, CSV is the default interchange format inside ETL (extract, transform, load) pipelines and data platforms. Tools like dbt, Airflow, and pandas all read CSV natively, and converting your Excel source to CSV gives those tools a file they can parse with no special drivers. If your workbook contains multiple sheets you will still need to export each one separately - a CSV file holds exactly one table, which is precisely what makes it predictable for a pipeline.
When the destination is code rather than a database, keep in mind that CSV carries no type information: a column that Excel knew was a date arrives as text, and your pipeline decides how to parse it. That is usually the right design, because the pipeline - not the file - should own the typing rules.
References and further reading
Import commands and behaviors described here were confirmed against the PostgreSQL, MySQL, and BigQuery documentation, and the conversion output was validated using SheetJS. See the primary sources below.
- SheetJS - SheetJS documentation
- PostgreSQL - COPY command reference
- Google Cloud - Loading CSV data into BigQuery
- IETF - RFC 4180: CSV file format
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Convert a file now →This guide was written by The File Content Conversion Team and published on February 10, 2026. Every conversion step it describes was tested with the actual tool on this site before publishing - in current versions of Chrome, Firefox, and Safari - and each guide documents exactly what its converter preserves and what it strips. Primary sources are linked in the “References and further reading” section above.