What Is a Parquet File? A Plain-English Guide

February 25, 2026 · 5 min read

If you work with data, you've probably seen .parquet files. They're everywhere — in Spark jobs, data lakes, machine learning pipelines, and analytics platforms. But what exactly are they, and why should you care?

The One-Sentence Answer

A Parquet file stores data in columns instead of rows, which makes analytical queries (like "what's the average salary?") dramatically faster and files dramatically smaller.

Row Storage vs. Column Storage

Imagine a spreadsheet with 1 million employees and 50 columns. In a traditional format like CSV:

• Data is stored row by row: all 50 fields for employee #1, then all 50 for employee #2, etc.
• To calculate average salary, the computer must read every single row — all 50 columns — just to extract one column.

In Parquet (columnar storage):

• Data is stored column by column: all 1 million salary values together, all names together, etc.
• To calculate average salary, it reads only the salary column — skipping the other 49 columns entirely.

Parquet vs. CSV: A Comparison

Feature	CSV	Parquet
Storage layout	Row-based	Column-based
File size (typical)	100 MB	10-30 MB (compressed)
Read speed (single column)	Slow (reads all columns)	Fast (reads only needed columns)
Schema	No (just text)	Yes (typed columns with metadata)
Human readable	Yes (open in Notepad)	No (binary format)
Best for	Small data, quick sharing	Analytics, data pipelines, ML

Why Is Parquet So Small?

Three reasons:

1. Column compression — Values in a column are usually similar (all ages are 20-80, all countries are from a fixed list). Similar values compress extremely well.
2. Encoding — Parquet uses smart encodings like dictionary encoding (replace repeated strings with tiny numbers) and run-length encoding (store "USA, USA, USA, USA" as "USA × 4").
3. No wasted text — CSV stores numbers as text ("12345" = 5 bytes). Parquet stores them as binary (4 bytes for any integer).

When Should You Use Parquet?

• ✅ Analytical queries (aggregations, filters, joins)
• ✅ Data pipelines (Spark, Pandas, DuckDB, BigQuery)
• ✅ Machine learning datasets
• ✅ Data lakes (S3, GCS, Azure Blob)
• ❌ Human-editable config files (use JSON/YAML)
• ❌ Tiny datasets under 1000 rows (CSV is simpler)
• ❌ Streaming/append-heavy workloads (use databases)

How to View a Parquet File

Since Parquet is binary, you can't open it in a text editor. Options:

• Python: import pandas; df = pandas.read_parquet("file.parquet")
• Command line: duckdb -c "SELECT * FROM 'file.parquet' LIMIT 10"
• Browser: Drag and drop into FileScope for instant visualization

Key Takeaway

Parquet is the default file format for modern data work. If you're dealing with datasets larger than a few thousand rows and doing any kind of analysis, switching from CSV to Parquet will make your files 3-10x smaller and your queries 10-100x faster. It's one of those rare upgrades that's all upside.