Parquet to CSV

Universal Format Converter

Convert between CSV, TSV, JSON, and Excel. Supports multiple files, primary data, and batch operations.

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AboutParquet to CSV

Convert Apache Parquet columnar files into CSV rows, and CSV back into Parquet — the standard format for data engineering, Spark, and pandas pipelines.Category: Conversion & Export
Previewing a Parquet file from a data lake without spinning up SparkConverting a pandas DataFrame export to CSV for a non-technical teammateTurning cleaned CSV data back into Parquet for a pipeline
The Parquet to CSV tool is compatible with:Apache SparkpandasDuckDBData Lakes. This tool alone can help you manage and analyze your data effectively. However, a composed workflow using NDJSON TO CSV, AVRO TO CSV, MULTIWAY CONVERTER may provide even more powerful data processing capabilities. Consider exploring these related tools for a comprehensive data solution.
Parquet is the de-facto columnar storage format across data engineering, analytics warehouses, and ML pipelines (Spark, pandas, DuckDB), chosen precisely because it's compact and fast for machines — not something you can casually open and eyeball. This tool reads a Parquet file's row groups and columns directly in your browser and flattens them into ordinary CSV rows, handling the full range of Parquet types and compression codecs. It also writes CSV data back into a real Parquet file, inferring column types automatically, useful when you've cleaned or filtered data in a spreadsheet and need it back in columnar form for a pipeline that expects Parquet input. The Parquet library only loads when you actually open this tool, so it never slows down the rest of the site for people who aren't converting Parquet files.

Frequently Asked Questions:

Does this handle large Parquet files?

It reads row groups and columns directly, so typical analytical Parquet files work well — very large multi-gigabyte files may be slow since everything runs in your browser tab rather than a distributed engine.

What happens to Parquet-specific types like nested structs?

Standard primitive types (strings, numbers, booleans, dates) convert cleanly to CSV columns; deeply nested struct or list columns are preserved as their JSON representation in the cell rather than being split into new columns.

Does converting CSV to Parquet require me to define a schema?

No — column types are inferred automatically from your data, which keeps the process one-click but means you should double-check inferred types (e.g. numeric-looking IDs) before feeding the file into a strict schema pipeline.


Developed by HowToCSV for our valued CSV Haters worldwide.