A Parquet file (extension .parquet) is an open, binary file format for storing tabular data — rows and columns — built for analytics. It was created in the Apache Hadoop ecosystem and is now one of the most common formats for data engineering, data science, and data lakes. Unlike CSV, you can't read a Parquet file in a text editor: it's compressed and stored column-by-column. The easiest way to look inside one is to open it in the web viewer.
Columnar storage, explained
Most formats (like CSV) store data row by row: all the fields of row 1, then all of row 2, and so on. Parquet stores it column by column: all the values of the first column together, then the next column, and so on.
That one change has big consequences. Because values in a column are all the same type and often similar, they compress extremely well. And when a query only needs three columns out of fifty, the engine reads just those three and skips the rest — which is why analytics over Parquet is so much faster than over CSV.
What makes Parquet useful
- Small. Columnar layout plus compression (Snappy, ZSTD, and others) makes Parquet far smaller than the equivalent CSV or JSON.
- Fast. Column pruning and built-in statistics let engines skip data they don't need.
- Typed. Each column has a real data type and a schema, so numbers, dates, and booleans are preserved exactly.
- Portable and open. It's an open standard read by Spark, pandas, Polars, BigQuery, Snowflake, and many more.
- Self-describing. The schema and column statistics live inside the file's footer.
Parquet vs CSV vs JSON
- CSV — plain text, universal, human-readable, but large, untyped, and slow to scan. Good for sharing small tables.
- JSON — flexible and great for nested data and APIs, but verbose and untyped for tabular use.
- Parquet — compact, typed, and fast for analytics, but binary and not human-readable.
A common workflow is to keep working data in Parquet and convert to CSV or JSON only when a tool or a colleague needs it. You can do all of those conversions here: Parquet to CSV, Parquet to JSON, CSV to Parquet, and more on the converters page.
How do I open a Parquet file?
You don't need Python or pandas just to look inside one. Open the online Parquet viewer, drag your .parquet file in, and it loads instantly — read locally in your browser. You can browse the rows, inspect the schema, filter and sort, run SQL, and export to another format. The file's contents never leave your device.
