Updated 2026-10-15
CSV vs Parquet: Which Format Should You Use?
For simple data exchange and human readability, choose CSV. For analytical processing of large datasets with efficient storage, choose Parquet.
Format Overview
CSV (Comma-Separated Values) is a plain-text format where each line is a data record and fields are separated by commas. It has been widely used for decades due to its simplicity and broad compatibility.
Parquet is a columnar storage format developed as part of the Apache Hadoop ecosystem. It stores data in columns rather than rows, enabling efficient compression and encoding schemes that reduce storage needs and improve query performance.
Readability and Compatibility
CSV files can be opened, read, and edited with any text editor or spreadsheet application. This universal compatibility makes CSV ideal for sharing data across different systems and with non-technical users.
Parquet files are binary and require specific tools to read. While less accessible for casual inspection, Parquet integrates seamlessly with big data platforms and analytical engines that support the format.
Storage Efficiency
CSV has no built-in compression, so file size grows linearly with data volume. For large datasets, this can result in significant storage requirements.
Parquet employs compression and encoding techniques that can substantially reduce storage needs, often by 75% or more compared to uncompressed CSV, especially for repetitive data patterns common in analytical workloads.
Performance Characteristics
CSV performance is limited by its row-based structure. To read specific columns, the entire file must be parsed sequentially, which can be inefficient for wide tables when only few columns are needed.
Parquet's columnar design allows reading only the columns needed for a query, significantly reducing I/O for analytical workloads. This column pruning capability makes Parquet particularly effective for aggregate operations and filtering.
Data Types and Schema
CSV stores all data as text strings. Data types must be inferred or specified by the consuming application, which can lead to inconsistencies and requires additional parsing logic.
Parquet preserves data types and schema information within the file itself. This self-describing nature ensures type safety and reduces the risk of data interpretation errors between systems.
Use Case Recommendations
Choose CSV for:
- Simple data exchange between systems
- Human-readable data files
- Compatibility with basic tools and scripts
- Small to medium datasets where simplicity trumps efficiency
Choose Parquet for:
- Large-scale analytical processing
- When storage efficiency is important
- Working with big data frameworks (Spark, Hive, etc.)
- Scan-heavy workloads benefiting from column pruning
Frequently asked questions
What is the main difference between CSV and Parquet?
CSV is a row-based plain text format where each line represents a record and fields are separated by commas. Parquet is a columnar storage format that stores data in columns rather than rows, optimized for analytical processing with efficient compression and encoding.
When should I use CSV instead of Parquet?
Use CSV when you need human-readable data, simple data exchange between systems, or compatibility with basic tools like text editors and spreadsheets. CSV is ideal for small to medium datasets where readability and simplicity are priorities.
When should I use Parquet instead of CSV?
Use Parquet for large datasets requiring efficient analytics, when storage space is a concern due to its compression capabilities, or when working with big data frameworks like Apache Spark. Parquet excels in scan-heavy analytical workloads.
Is Parquet human-readable like CSV?
No, Parquet is a binary format and not human-readable. Unlike CSV which can be opened in any text editor, Parquet requires specialized tools to read its contents. This trade-off enables Parquet's superior compression and performance characteristics.
Related reading
Related reading
Format Conversion — other guides that pair well with this one.
- CSV vs ODS: Which Spreadsheet Format Should You Use?
- CSV vs JSON
- How to Clean Dirty CSV Data
- Remove Duplicates from CSV
Browse all guides in the NoCodeCSV blog.