Is Parquet faster than CSV?

For analytics queries, orders of magnitude faster. Parquet's columnar format means you only read the columns you need, and compression reduces I/O. For full-table scans of small files, the difference is minimal.

Can I convert CSV to Parquet?

Yes. DuckDB: COPY (SELECT * FROM read_csv('file.csv')) TO 'file.parquet'. Python: pandas.read_csv('file.csv').to_parquet('file.parquet'). Polars is even faster for this conversion.

When should I still use CSV?

For small datasets (<100MB), data exchange with non-technical users, Excel imports, and quick scripts. CSV's simplicity and universal support make it the default for simple tabular data sharing.

Last updated: April 12, 2026

CSV vs Parquet — Row-Based vs Columnar Data Format in 2026

Quick Answer

CSV is a simple, human-readable text format for tabular data. Parquet is a columnar binary format that is 2-10x smaller and orders of magnitude faster for analytics queries. CSV for simple data exchange; Parquet for analytics and big data.

CSV vs Parquet — Side by Side

Feature	CSV	Parquet
Format	Text (comma-separated)	Binary (columnar)
File Size	Large — no compression by default	2-10x smaller (Snappy/Zstd compression)
Schema	No schema — headers only	Embedded schema with types
Column Selection	Must read entire file	Read only needed columns
Analytics Speed	Slow — scan entire file	Fast — column pruning, predicate pushdown
Human Readable	Yes — open in any editor	No — binary format
Ecosystem	Universal — Excel, databases, scripts	Big data — Spark, DuckDB, Polars, BigQuery

Verdict

Use CSV for simple data exchange, small datasets, and when human readability matters. Use Parquet for analytics, data lakes, and any dataset over 100MB where query performance and storage efficiency matter.

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