Convert CSV to Parquet Online

Converting CSV to Parquet is one of the most common steps in building a data pipeline. Parquet's columnar layout means analytical queries that read only a few columns run far faster than on CSV, and the built-in compression (Snappy by default) typically shrinks a CSV file to 20–30% of its original size. The column schema — names and data types — is embedded in the Parquet file footer, so downstream tools like DuckDB, Spark, Athena, and BigQuery read types without guesswork.

Typical use cases include archiving large CSV exports from databases or SaaS tools, uploading data to S3 or GCS for querying with Athena or BigQuery, and preparing datasets for pandas, polars, or DuckDB analysis workflows. If you regularly export the same CSV and re-upload it to an analytics system, converting to Parquet once reduces storage costs and query time every time the data is read.

This tool infers column types from your CSV data — integers, floats, strings, booleans, and dates are detected automatically. For best results, ensure numeric columns do not contain mixed types (for example, empty strings where you expect nulls — use the Fill Empty Values tool first if needed). The output is a single Parquet file ready for use in any Parquet-compatible tool.