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Parquet to CSV Converter
Convert Parquet to CSV online — free and unlimited. No sign-up, no limits. Upload your Parquet file and download CSV instantly.
Upload your Parquet file
Drag & drop or click to browse · Max 500 MB
How to convert Parquet to CSV
Upload your file
Drag & drop or click to browse. Supports files up to 500 MB.
Adjust settings if needed
Schema and types are detected automatically — no configuration required.
Preview & download
Inspect the first rows in your browser, then download the converted file — free.
About converting Parquet to CSV
Parquet is columnar and binary. That is why it is the storage format for essentially every data lake — it compresses far better than CSV and lets a query engine read one column without touching the rest — and also why you cannot simply open one to check what is in it.
The usual answer is to spin up Spark, or install PyArrow and write a script, for what is really a five-second question. This does that conversion in the browser: upload the file, download the CSV, no environment to set up.
The conversion is Arrow-native and column-wise, in C, rather than a Python row loop. That is an order of magnitude faster on a real file and, more importantly, means memory does not scale with the row count — the same reason Parquet exists in the first place.
What actually changes
- Arrow-native
- Read and written column-wise in C, never materialised as Python objects — an order of magnitude faster than a row loop.
- Row count known
- Parquet metadata carries the row count, so the progress bar is accurate rather than estimated.
- Schema
- Column names become the CSV header; types are flattened to their text representation.
- Compression
- Snappy, gzip and zstd-compressed files are all read transparently.
What it cannot do
Every conversion loses something. These are the limits of this one, stated up front rather than discovered in the output.
- CSV has no types, so the schema is lost. Convert to JSON or Excel if the distinction matters, or keep the Parquet.
- Nested and repeated fields (structs, lists, maps) are flattened to their text representation.
- A partitioned dataset is a directory of files. Convert one file at a time, or concatenate first.
- CSV output is much larger than the Parquet input — often by an order of magnitude.
Frequently Asked Questions
Do I need Spark or Python to open a Parquet file?
Not for this. The conversion runs server-side with Arrow, so there is nothing to install — which is usually the whole reason someone wants to look inside a Parquet file in the first place.
Will the CSV be much bigger?
Yes, often by an order of magnitude. Parquet is columnar and compressed; CSV is uncompressed text. That is the trade you are making for something you can open in a spreadsheet.
What happens to nested columns?
Structs, lists and maps are flattened to their text representation, because CSV has no way to express nesting. Convert to JSON instead if the structure matters.
Are the column types preserved?
No — CSV records no types at all. If you need the schema, JSON or Excel preserve more of it, and Parquet itself preserves all of it.