Convert Excel to Parquet Online
Convert Excel files to Parquet format directly in your browser. No upload required — your data never leaves your device.
About converting Excel to Parquet
Teams that receive monthly reports, supplier price lists or finance exports as Excel files often convert them to Parquet before loading them into S3, BigQuery, Athena, Snowflake or a local DuckDB or pandas workflow. Parquet stores a schema, so the next tool reads typed columns instead of guessing from cell text each time.
The size gain is smaller than with CSV input. An XLSX file is already a ZIP archive, so the Parquet file may be only somewhat smaller. The real benefit is typed columns and fast reads of individual columns in analytics tools.
Types are inferred from the cell text Excel displays. A column of plain numbers becomes BIGINT or DOUBLE. A column formatted as currency or percent becomes VARCHAR, because "$89.99" and "25%" are not numbers. Cells with a date format become DATE, or TIMESTAMP when they include a time, however Excel displays them. Dates typed as text are only guessed at, and text such as 1-Jun-26 stays VARCHAR. One error value such as #N/A or #DIV/0! turns its whole column into text. Only the first sheet is read, and merged cell areas leave NULLs below and to the right of the first cell. Format number columns as plain numbers in Excel before converting if the types matter.
Not sure which format you need? Read the Excel vs CSV comparison.
Worked example
A small orders export with an ID, a customer name, a date, an amount (one missing) and a true/false flag, converted with the default settings.
Input (Excel)
Excel workbook, first sheet (Sheet1)
| order_id | customer | order_date | amount | shipped |
|---|---|---|---|---|
| 1001 | Acme Ltd | 2026-03-02 | 249.5 | true |
| 1002 | Brightside Co | 2026-03-02 | 1200 | false |
| 1003 | Acme Ltd | 2026-03-05 | 89.99 | true |
| 1004 | Northwind | 2026-03-07 | NULL | false |
Output (Parquet)
Parquet file (binary, columnar) — shown as a table with its schema
| order_id | customer | order_date | amount | shipped |
|---|---|---|---|---|
| 1001 | Acme Ltd | 2026-03-02 | 249.5 | true |
| 1002 | Brightside Co | 2026-03-02 | 1200 | false |
| 1003 | Acme Ltd | 2026-03-05 | 89.99 | true |
| 1004 | Northwind | 2026-03-07 | NULL | false |
Schema: order_id BIGINT, customer VARCHAR, order_date DATE, amount DOUBLE, shipped BOOLEAN
What changes when you convert Excel to Parquet
- Header cells in row 1 become Parquet column names. An empty header cell gets a generic name such as column1.
- Plain whole numbers become BIGINT and decimals become DOUBLE. Currency and percent formats stay VARCHAR.
- Real date cells, like order_date in the example, become DATE in any display format. Date-times become TIMESTAMP.
- TRUE and FALSE become BOOLEAN. Empty cells become NULL, like the amount for order 1004.
- Formulas are stored as their cached result. The formula text is not kept.
Your file is processed locally in your browser and is never uploaded. The free limit is 50 MB per file; larger files work if your device has the memory for them.
Frequently Asked Questions
Why is my Excel amount column VARCHAR in Parquet?
The cells use a currency, accounting or percent format, or at least one cell holds text or an error like #N/A. Set the column to General or Number in Excel and fix error cells, then convert again. You can also cast the column later with the Cast Column Types tool.
Does the Parquet file keep Excel number formats?
No. Parquet stores values and types, not display formats. A DOUBLE column has no notion of two decimal places or a currency symbol. Formatting has to be applied again in whatever tool reads the file.
Can I convert several sheets into one Parquet file?
Not in one step. Convert each sheet separately by moving it to the first tab, then combine the Parquet files with the Merge tool or a UNION ALL in the SQL Query tool.
What is Parquet format?
Parquet is an open-source columnar storage format designed for efficient analytics. It compresses far better than CSV and is natively supported by Spark, Athena, BigQuery, Pandas, and DuckDB.
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