Last Updated on: 12th February 2026, 07:33 pm

If you work in data science, big data analytics, machine learning, or even simple reporting, you have probably heard this debate many times: CSV vs Parquet vs Arrow. People talk about performance, compression, memory usage, columnar storage, and it can start to sound very technical very fast. But honestly, most people just want to know one thing. Which one should I use and why.

Let’s start from the beginning.

What is CSV File Format?

CSV stands for Comma Separated Values. It is one of the oldest and most widely used data storage formats in the world. Almost every tool supports CSV. Excel supports it. Python supports it. R supports it. Databases can export and import it.

A CSV file is basically plain text. Each row is a new line. Each column is separated by a comma. That’s it. No special compression. No schema metadata stored inside the file normally. Just text separated by delimiters.

For example:

name,age,city
Ali,25,Lahore
Sara,30,Karachi

It is simple. Very simple. And that is why it became popular.

But simplicity has cost.

Advantages of CSV Format

One big advantage of CSV file format is compatibility. Almost every software can read CSV. You don’t need special libraries most of the time.

Another advantage is human readability. You can open CSV in a text editor and understand what is inside. That is not possible with Parquet easily.

CSV files are also easy to generate. Even basic scripts can produce CSV output.

For small datasets, CSV is perfectly fine. There is no need to over engineer storage if your file is just few MB.

Disadvantages of CSV Format

Now the problems.

CSV does not store data types. Everything is text. So when you load CSV into pandas or Spark, it needs to guess the data type. That guessing sometimes goes wrong.

CSV is row based format. When you want to read only one column from a large CSV file, you still need to scan entire file. That wastes time.

CSV files are larger in size compared to compressed columnar formats like Parquet. For big data systems, storage cost matters.

Also CSV does not support schema evolution easily. If columns change, things can break.

So while CSV is simple, it is not optimized for analytics workloads.

What is Parquet File Format?

Apache Parquet is a columnar storage format designed for big data processing frameworks like Hadoop, Spark, and modern analytics systems.

Unlike CSV, Parquet stores data by columns instead of rows. This means values from same column are stored together physically.

Why is that important?

Because in analytics, we usually query few columns, not entire dataset. Columnar storage allows reading only required columns, which improves performance.

Parquet also supports compression automatically. That means smaller file size and lower storage cost.

It also stores schema metadata inside file. So no guessing of data types.

Already you can see, this is more advanced than CSV.

Advantages of Parquet Format

One major benefit of Parquet is performance optimization for analytical queries. If you run SQL queries on data lake, Parquet is usually much faster than CSV.

Parquet uses columnar compression. Similar values in a column compress better. For example, if a column has many repeated category values, compression ratio is very good.

Parquet also supports predicate pushdown. That means system can skip entire chunks of data if they don’t match filter condition. CSV cannot do that.

Another big advantage is schema support. Data types are preserved. This reduces errors during data loading.

For big data pipelines, Parquet is almost default choice now.

Disadvantages of Parquet Format

Parquet is not human readable. You cannot open it in simple text editor.

It requires specific libraries like PyArrow or Spark to read and write. For beginners, that can feel complicated.

For very small datasets, Parquet overhead might not give big advantage compared to CSV.

Also debugging Parquet issues sometimes takes more effort because you cannot inspect file directly easily.

So it is powerful, but not always simplest option.

What is Apache Arrow?

Apache Arrow is slightly different from CSV and Parquet. It is not just a file storage format. It is more like in memory columnar data format designed for fast analytics.

Arrow focuses on high performance data interchange between systems. For example, passing data between Python and R without copying.

Arrow uses columnar memory layout, similar idea like Parquet, but optimized for in memory processing instead of disk storage.

Arrow can also serialize data to disk, but its main power is in memory speed.

This is why many modern data tools use Arrow internally.

Advantages of Apache Arrow

Apache Arrow provides zero copy reads in many cases. That means less memory duplication and faster processing.

It is very useful in machine learning pipelines where data moves between frameworks.

Arrow improves interoperability between systems. For example pandas, Spark, and even GPUs.

Because Arrow is columnar in memory, vectorized operations become faster.

For high performance analytics and data science workflows, Arrow gives serious speed improvement.

Disadvantages of Apache Arrow

Arrow is not as widely used as CSV for simple storage.

It is more technical and mostly used by developers working on analytics engines.

For simple data exchange, Arrow might be unnecessary complexity.

Also Arrow ecosystem still evolving compared to older formats.

CSV vs Parquet Performance Comparison

If you compare CSV vs Parquet performance for big data analytics, Parquet usually wins clearly.

Parquet reads faster because it loads only needed columns. CSV must read entire row.

Parquet compressed size is smaller, so disk IO is reduced.

In Spark benchmarks, Parquet can be 5x to 10x faster than CSV for analytical queries. Sometimes even more.

However, for very small files difference is small and maybe not noticeable.

Parquet vs Arrow: What is the Real Difference?

Many people confuse Parquet and Arrow because both are columnar.

Parquet is optimized for disk storage and long term storage in data lakes.

Arrow is optimized for in memory data representation and fast processing between systems.

Think like this.

Parquet is like warehouse storage. Arrow is like conveyor belt inside factory.

Both are important, but used differently.

CSV vs Parquet vs Arrow for Machine Learning Pipelines

If you build machine learning pipeline, file format matters.

CSV is simple but slow for large training datasets.

Parquet is better for storing training data in data lake.

Arrow is useful when transferring data between pandas, NumPy, and other libraries quickly.

Many modern ML systems use Parquet for storage and Arrow for in memory operations.

Which File Format is Best for Big Data Analytics?

For big data analytics workloads, Parquet is generally best choice.

It reduces storage cost, improves query performance, and integrates well with Spark, Hive, and cloud platforms like AWS and Azure.

CSV is rarely used in serious big data production environments now, except for initial ingestion.

Arrow is used under the hood in many engines but not always directly by user.

Storage Size Comparison: CSV vs Parquet

CSV files are larger because they store raw text without compression.

Parquet uses compression algorithms like Snappy and Gzip. That reduces file size significantly.

In some cases Parquet file can be 3x to 5x smaller than CSV.

Smaller size means lower cloud storage bill. That matters for companies.

Ease of Use Comparison

CSV is easiest to use. Anyone can open it.

Parquet requires more technical setup.

Arrow requires even deeper understanding of data engineering concepts.

So ease of use ranking is CSV first, Parquet second, Arrow third.

But performance ranking is opposite usually.

When Should You Use CSV?

Use CSV when:

• Dataset is small
• You need universal compatibility
• You want human readable file
• You are doing quick data sharing

CSV is still good for exporting reports and simple integrations.

When Should You Use Parquet?

Use Parquet when:

• You work with big data
• You use Spark or Hadoop
• You care about performance optimization
• You want compressed storage

Parquet is best file format for analytics heavy workloads.

When Should You Use Apache Arrow?

Use Arrow when:

• You need fast in memory data exchange
• You build analytics engines
• You move data between languages
• You want vectorized performance

Arrow is more advanced use case tool.

Final Thoughts on CSV vs Parquet vs Arrow

There is no one size fits all answer.

CSV is simple and universal but not optimized.

Parquet is powerful for storage and analytics.

Arrow is high performance in memory format.

Understanding difference between CSV, Parquet and Apache Arrow will help you design better data pipelines and avoid performance bottlenecks later.

Sometimes developers choose CSV just because it is easy. Later system becomes slow and expensive. Choosing right format early saves time and money both.

FAQs

1. What is the main difference between CSV and Parquet?

CSV is row based text format without compression and schema. Parquet is columnar compressed format with schema support and better performance for analytics.

2. Is Parquet faster than CSV?

Yes, especially for big data queries. Parquet reads only required columns and supports predicate pushdown.

3. What is Apache Arrow used for?

Apache Arrow is mainly used for fast in memory data processing and data interchange between systems.

4. Can Arrow replace Parquet?

Not exactly. Arrow is optimized for memory operations while Parquet is optimized for disk storage.

5. Why is Parquet smaller than CSV?

Because Parquet uses compression and columnar encoding which reduces file size.

6. Is CSV outdated?

Not fully. It is still widely used for small datasets and simple data sharing.

7. Which format is best for machine learning?

Parquet for storage, Arrow for in memory processing, depending on pipeline architecture.

8. Does Parquet support schema evolution?

Yes, Parquet supports schema evolution better than CSV.

9. Can Excel open Parquet files?

No, Excel does not directly support Parquet without additional tools.

10. Which format should beginners start with?

Beginners can start with CSV because it is simple. Later they should learn Parquet and Arrow for advanced analytics work.