Parquet Viewer
Open a .parquet file to see its schema and data as a table, without Python, Spark or DuckDB.
- Schema & metadata
- Snappy, Zstd, Gzip
- Sort & search
- No upload
Drop a Parquet file here. CSV, TSV, XLSX and Parquet all work.
What is a Parquet viewer?
Apache Parquet is a columnar storage format used across data engineering: Spark, Pandas, Polars, DuckDB, BigQuery, Snowflake and data lakes all read and write it. Being binary and compressed, it cannot be opened in a text editor or spreadsheet. This viewer reads Parquet in your browser with the open-source hyparquet library: it shows the schema (column names, physical and logical types, compression), row groups and file metadata, and the data itself as a table you can sort, search and export to CSV or JSON. Snappy, Gzip, Zstd, Brotli and LZ4 compression are supported.
How to open a Parquet file
- Choose Open File and pick the .parquet file, or drag it onto the page.
- Check the schema panel for columns, types and compression.
- Sort, search and inspect values in the table.
- Export to CSV, JSON or Excel.
Reading Parquet types
- INT64 values beyond JavaScript’s safe integer range are shown exactly, as text.
- Timestamps are shown in UTC as YYYY-MM-DD HH:MM:SS.
- Lists, maps and structs are shown as JSON.
- Up to 500,000 rows are loaded; the total row count comes from the file footer.
Why use this Parquet viewer
- Search, sort and inspectSort any column, search every cell, click a cell to see its full value, and get column stats: type, blanks, unique values, min, max, sum and average.
- Handles large filesOnly the rows on screen are drawn, so files with hundreds of thousands of rows scroll smoothly. Search and sort the whole file instantly.
- Export what you needDownload the data, or just the rows matching your search, as CSV, TSV, JSON, Excel or a Markdown table.
- Private by designThe file is read by your browser and never uploaded, so customer lists, payroll exports and financial data stay on your computer.
Files you can open
| Format | Extensions | Supported |
|---|---|---|
| CSV / TSV | .csv, .tsv, .txt | Comma, semicolon, tab or pipe; UTF-8, UTF-16 or Windows-1252 |
| Excel workbook | .xlsx, .xlsm | All sheets, cached formula values, date and percent formats |
| Apache Parquet | .parquet | Snappy, Gzip, Zstd, Brotli and LZ4; schema and row groups |
Common uses
- Data engineering. Peek at a pipeline output without spinning up a notebook.
- Analysts. Open a Parquet export without Python.
- Debugging. Check types, nulls and compression of a file.
- Sharing. Convert a Parquet extract to CSV or Excel for colleagues.
Related tools: CSV to JSON, JSON to CSV, JSON viewer and HTML table to Markdown.
Example: an orders extract
The sample Parquet file has 20 rows and 9 columns written by PyArrow with Zstd compression; Date is a DATE logical type and Paid a BOOLEAN.
What to expect from the result
The first 500,000 rows are loaded. Encrypted Parquet files are not supported. Very wide nested schemas are flattened to top-level columns, with nested values shown as JSON.
Parquet Viewer questions
How do I open a Parquet file without Python?
Open it here: the file is read in your browser and shown as a table with its schema. No Python, Spark or DuckDB is needed.
Which compression codecs are supported?
Uncompressed, Snappy, Gzip, Zstd, Brotli and LZ4 (raw and Hadoop).
Can I convert Parquet to CSV?
Yes. Choose CSV under Export to download the loaded rows. JSON and Excel are also available.
How large a file can I open?
Files of several hundred megabytes open on most computers, limited by your browser’s memory. Rows are drawn as you scroll, so large tables stay responsive.
Is my file uploaded?
No. The file is opened and processed by your browser on your own device. Nothing is sent to a server and the page works offline once loaded.