CSV Tools

Parquet Viewer

View parquet files online — sortable table plus full schema, types, and row counts

Need it as a spreadsheet? Use the Parquet to CSV converter. Creating parquet files from CSV? Use the CSV to Parquet converter.

Parquet Input

Drop your .parquet file here

or click to browse

How to Use This Tool

This tool opens Apache Parquet files in a spreadsheet-style table with sortable columns, alongside a schema panel showing every column’s name, physical and logical types, compression codec, total row count, and row-group count.

Everything runs directly in your browser — your parquet file is never uploaded, so your data stays private. Large files are previewed efficiently: only the first 10,000 rows are loaded up front, while the schema always reflects the whole file.

  1. Drop your parquet file — Drop a .parquet file onto the upload area or click to browse.
  2. Inspect schema and rows — Review column types, codecs, and counts in the schema panel, then browse the table. Click any column letter header to sort ascending, again for descending, and a third time to restore the original order.
  3. Load all rows if needed — For files over 10,000 rows, use the Load all rows action in the preview banner to read the entire file.

FAQ

4 questions
Does this parquet viewer upload my file?
No. It runs entirely in your browser using JavaScript. Your parquet file is never sent to a server and never leaves your device.
How does the preview work for large files?
The viewer reads only the file footer and the row groups needed for the first 10,000 rows. The schema panel and total row count always reflect the whole file. Use the Load all rows action to read everything — this may be slow for very large files.
Which compression formats are supported?
Uncompressed, Snappy, Gzip, Zstandard, Brotli, and LZ4 — covering parquet files written by pandas, Spark, DuckDB, and Arrow.
Can I open a parquet file without Spark or pandas?
Yes — that's the point of this tool. Drop the file onto the page and see its rows and schema instantly, with no code, notebooks, or installs required.

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