What is Parquet? How it differs from CSV and when to use it

Parquet is a file format designed for analyzing large datasets. Where CSV lays data out one row at a time (row-oriented), Parquet groups the values of each column together (columnar). That single difference is what drives the size and speed gains.

Say you only want the total of a sales column. With a columnar layout, you read just that column and skip the rest — so Parquet reaches the answer with far fewer reads than a CSV that has to scan every row to the end.

Parquet files are dramatically smaller than CSV. Similar values sit together by column, so compression works well — the same data is often 5–10× smaller than CSV, cutting both storage and transfer costs.

The other difference is that Parquet carries types. In CSV everything is text and the reader guesses whether a value is a number or a date. Parquet records a type per column (number, string, date, and so on), so the type-coercion, garbling, and delimiter problems that plague CSV largely go away.

In environments billed by data scanned — S3 + Athena, or BigQuery — converting to Parquet reduces the scan and lowers the cost of every query. For large batch jobs in Spark, Redshift Spectrum, or EMR, Parquet input tends to run faster too.

The more these hold — you query the same data repeatedly, there are many columns but you only use some, the data is large enough that transfer and storage cost matters — the more Parquet pays off. Files that live in a data platform long-term are best stored as Parquet from the start.

Parquet is a binary format, so you cannot open it directly in a text editor or an ordinary spreadsheet. When you want to eyeball the data in Excel, hand it to a non-engineer, or do a quick visual check, CSV is far easier.

Even just peeking at what is inside a Parquet file on S3 is often faster by converting to CSV and opening it locally than by spinning up a query engine. CSV also suits debugging a pipeline or sharing a quick extract with a colleague.

Our Parquet to CSV and CSV to Parquet tools both process in the browser. Files are never sent to a server, so you can work with production or sensitive data directly.

Remember it as: Parquet → CSV when you want to quickly check what you pulled from S3, and CSV → Parquet when you want to tidy a locally built CSV before loading it into a platform — no dedicated tooling to launch.

Parquet is a columnar, typed format that beats CSV on size, speed, and query cost. CSV is the one humans open and share. 'Parquet for what the platform runs, CSV for checking and sharing' is the practical split.

Parquet ⇄ CSV conversion is free, signup-free, and in-browser on Filewisp — so you can take the best of both while you work.