Parquet vs CSV
Parquet and CSV represent two different philosophies in data storage: one optimised for machines and analytical engines, the other optimised for humans and universal compatibility. Understanding the trade-offs determines which to use — and when to convert.
What is Parquet?
Apache Parquet is an open-source binary columnar storage format from the Apache project. It stores data column by column rather than row by row, embeds the column schema (names and types) in the file, and applies efficient compression codecs like Snappy or Zstandard. This design makes Parquet the preferred format for analytical workloads — data lakes, query engines, and large-scale processing.
Parquet is the native format of AWS Athena, Google BigQuery, Apache Spark, Databricks, Delta Lake, and Apache Iceberg. It is also the default storage format for DuckDB and the preferred exchange format for pandas and polars. If you work in a modern data stack, you are almost certainly working with Parquet files.
What is CSV?
CSV (Comma-Separated Values) is plain text — each line is a row, commas separate column values. There is no binary encoding, no schema requirement, and no special software needed to read or write it. CSV is the default export format from virtually every database, reporting tool, SaaS platform, and spreadsheet application in existence.
CSV's universality is its defining characteristic. A CSV file opens in Excel, reads in Python, loads into PostgreSQL, and parses in JavaScript without any configuration. The tradeoff is efficiency: CSV stores no type information, applies no compression, and requires a full file scan for every query.
Parquet vs CSV: Key Differences
| Feature | Parquet | CSV |
|---|---|---|
| File type | Binary (columnar) | Plain text |
| Human readable | No — requires a tool | Yes — opens in any text editor |
| Schema | Embedded and enforced | None (inferred on read) |
| Compression | Built-in (Snappy, Zstd, Gzip) | None by default |
| Typical file size | 10–30% of equivalent CSV | 100% |
| Query performance | Fast (columnar pruning) | Slow on large files (full scan) |
| Tool support | Data engineering tools | Universal |
| Append records | Requires rewriting the file | Simple (append lines) |
When to use Parquet
- ✓Storing data in a cloud data lake or object storage (S3, GCS)
- ✓Querying with DuckDB, Athena, BigQuery, Spark, or pandas/polars
- ✓Archiving large datasets to save storage costs
- ✓Preserving accurate column types across systems and pipeline stages
- ✓Long-term storage where file size and query speed matter
When to use CSV
- ✓Sharing data with colleagues who use Excel or Google Sheets
- ✓Loading into a system that only accepts plain-text input
- ✓Inspecting or debugging data — CSV is immediately readable
- ✓Exporting for a one-off analysis without engineering tooling
- ✓Small files where compression savings are not significant
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