CSV vs Parquet vs Arrow: Which Data Storage Format Should You Use?

Choosing the right data storage format is critical in modern data workflows. CSV vs Parquet are two of the most commonly used formats, each with unique strengths, weaknesses, and ideal use cases. In this guide, we also cover Apache Arrow and help you decide which format suits your needs in 2026

In 2026, three formats are most commonly used in the data engineering ecosystem: CSV, Parquet, and Apache Arrow. Each has its strengths, limitations, and ideal use cases. This guide will help you decide which format to use based on data size, read/write speed, analytics needs, and computational efficiency.

Understanding the Basics

Before comparing, let’s define each format:

1. CSV (Comma-Separated Values)

CSV is one of the oldest and simplest data storage formats. It stores data as plain text, with rows separated by newlines and columns separated by commas.

Pros:

• Human-readable and easy to understand

• Widely supported across programming languages and tools

• Simple to share and open in Excel, Google Sheets, or text editors

Cons:

• Inefficient for very large datasets

• No support for data types (everything is text)

• Slow read/write for big data processing

• Lacks compression, leading to larger file sizes

Ideal Use Cases:

• Small datasets

• Data exchange between systems

• Quick prototyping or debugging

2. Parquet

Apache Parquet is a columnar storage format designed for big data processing. It stores data by columns rather than rows, enabling faster analytics and compression.

Pros:

• Columnar storage improves query performance for analytics

• Supports data types, nested structures, and complex data

• Highly compressed, saving storage space

• Optimized for distributed systems like Spark, Hive, and Presto

Cons:

• Not human-readable

• Slower for row-based operations like streaming inserts

• More complex to implement than CSV

Ideal Use Cases:

• Data warehouses and analytics pipelines

• Big data frameworks (Apache Spark, Hadoop)

• Scenarios requiring heavy column-wise computation

3. Apache Arrow

Apache Arrow is an in-memory columnar format designed for high-speed analytics and zero-copy data exchange. Unlike CSV or Parquet, Arrow focuses on fast computation in memory rather than long-term storage.

Pros:

• Extremely fast for in-memory operations

• Enables zero-copy data transfer between languages (Python, R, Java)

• Ideal for machine learning, pandas, and GPU-accelerated pipelines

• Supports nested and complex data structures

Cons:

• Primarily in-memory, so not ideal for long-term storage

• Requires compatible tools and libraries

• More complex to implement than CSV

Ideal Use Cases:

• Real-time analytics and data processing

• Machine learning pipelines with pandas, Spark, or RAPIDS

• Interoperable data transfer between systems without serialization overhead

CSV vs Parquet vs Arrow: Key Comparison

When to Use Each Format

1. CSV:

   • Small datasets or quick data exchange

   • Debugging or testing small pipelines

   • Simple scripts or legacy systems

2. Parquet:

   • Data warehouses and ETL pipelines

   • Scenarios needing fast column-based queries

   • Storage efficiency and cost reduction for big data

3. Arrow:

   • High-performance, in-memory analytics

   • Interoperable machine learning pipelines

   • Scenarios requiring GPU acceleration or cross-language processing

Practical Examples

CSV Example

Use case: Small datasets, quick load and share.

Parquet Example

Use case: Analytics pipelines with Spark or distributed systems.

Arrow Example

Use case: Fast in-memory operations and ML pipelines.

Key Takeaways

• CSV: Best for small, human-readable datasets; simple to share; slow for large data.

• Parquet: Best for analytics and big data; columnar storage and compression; not human-readable.

• Arrow: Best for in-memory computation, machine learning, and fast cross-language data sharing; not meant for long-term storage.

Rule of Thumb:

• Use CSV for simple exchange or testing

• Use Parquet for storage and analytics in big data pipelines

• Use Arrow for high-speed, in-memory processing and ML workflows

Conclusion

Choosing the right data format is critical for performance, efficiency, and scalability. In 2026, most production pipelines combine formats: raw CSV for data ingestion, Parquet for storage and analytics, and Arrow for fast, in-memory computation in ML models. Understanding the strengths and weaknesses of each format will help you design faster, more efficient, and scalable data workflows.