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CSV is easy to open and share. Parquet is designed to store analytical data efficiently. Here is what actually separates the two formats, why it matters for big data, and when you should use each.
October 2026
There is a good chance your first dataset was a CSV file. You downloaded it, opened it in Excel or Google Sheets, and started exploring the data. Simple, familiar, and convenient.
Now imagine that the same dataset has grown from 10,000 rows to 100 million. Your file takes longer to load, your analysis tool uses more memory, and even a simple query starts feeling unnecessarily expensive.
This is where another file format enters the conversation: Apache Parquet.
CSV and Parquet can store the same logical data, but they organise it very differently. That difference affects file size, how much data an analytical engine needs to read, and how efficiently queries can run.
So, is Parquet always better? Not quite. CSV still has important advantages, particularly when humans need to inspect, edit or exchange data.
Let's understand how both formats work, compare their strengths, and see which one makes sense for your data workflow.
CSV stands for Comma-Separated Values. It is a text-based format that represents tabular data using rows and fields separated by delimiters, commonly commas.
For example, a sales dataset might look like this:
order_id,product,quantity,revenue
1001,Laptop,1,65000
1002,Mouse,2,1200
1003,Keyboard,1,2500
Each line represents a record, and each field represents a value. Because the content is plain text, you can open a CSV file in a text editor, spreadsheet application or programming environment.
That simplicity is CSV's biggest advantage. You do not need a specialised reader just to understand what the file contains.
But CSV has a limitation that becomes important in analytics: the format does not inherently store a formal column schema with data types.
The value 65000 is simply text in the file. The tool reading it must decide whether it represents an integer, a decimal, an identifier or something else. Dates, missing values, quotation marks and embedded commas can also require careful handling.
That is manageable for small datasets. At scale, repeated parsing and type inference can add overhead.
Apache Parquet is an open-source, column-oriented file format designed for efficient storage and retrieval of analytical data.
The important word here is column-oriented.
Imagine a table containing one million transactions. Each record includes a transaction ID, customer ID, date, region, product, quantity and revenue.
If you only want total revenue by region, you do not necessarily need to read every column. You mainly need the region and revenue values.
Parquet organises data by columns within row groups, rather than simply writing each complete record as a line of text. It also stores metadata about the file and its columns, including information that compatible query engines can use to read data more selectively.
Parquet supports compression and encoding techniques that can reduce storage requirements. Repeated values, such as region names or category labels, may be represented efficiently depending on the data and encoding used.
Analytical workloads often scan many records but use only a subset of the available columns. A columnar format is well suited to that pattern.
For example, a query calculating total revenue by region might need only two columns from a table with twenty columns. A Parquet-aware engine can often read just those required columns instead of processing the entire dataset.
Engines such as DuckDB can also push supported filters into Parquet scans and use available metadata to skip portions of the data where possible. These optimisations can reduce unnecessary reading, although the actual benefit depends on the file layout and query.
| Feature | CSV | Parquet |
|---|---|---|
| Storage layout | Text-based, row-oriented records | Column-oriented data within row groups |
| Human readability | Easy to read in a text editor | Binary format that requires a compatible reader |
| Schema and types | Must generally be inferred or specified by the reader | Stores schema information and typed column metadata |
| Compression | Often compressed separately, for example as CSV.GZ | Supports column-level compression and encoding |
| Analytical queries | Often requires parsing rows and reading text fields | Can read selected columns and skip eligible data regions |
| Editing | Easy to inspect and modify manually | Usually modified through software |
| Common use cases | Data exchange, exports and small datasets | Data lakes, analytics pipelines and large datasets |
Neither format wins every category. CSV prioritises simplicity and interoperability, while Parquet is designed to make analytical storage and retrieval more efficient.
Let's use a practical example.
Suppose your company stores five years of sales data in a dataset containing 30 columns. You want to calculate revenue for each region during the previous month.
A straightforward CSV reader may need to parse records across the file to identify the relevant dates, regions and revenue values. Even if your query only needs three columns, a conventional CSV scan cannot use a native columnar layout to skip the other columns.
With Parquet, a compatible analytical engine can often read only the necessary columns. Depending on the metadata and layout, it may also skip row groups that cannot match the date filter.
This can reduce the amount of data read and processed.
Parquet can also reduce storage through compression and encoding. Smaller files may mean less data transferred from storage, particularly when working with remote data or cloud object storage.
However, Parquet is not automatically faster for every operation. File size, compression codec, row-group layout, query structure, hardware and the engine all affect performance. For a small file that you open once, CSV may be perfectly adequate.
Parquet often produces smaller files for analytical datasets, but there is no universal compression ratio.
Consider a dataset containing millions of records with repeated country names, product categories and status values. Column-oriented encoding and compression can represent these repeated values efficiently.
Now consider a dataset dominated by long, unique text fields or already compressed content. The size difference may be less dramatic.
The compression method also matters. Parquet supports multiple compression codecs, each with different trade-offs between file size and processing cost. A highly compressed file can save storage but may take more CPU time to decode.
The sensible approach is to test your own data. Convert a representative sample, compare the output sizes and measure query performance using the workload you actually run.
CSV is still a sensible choice in many situations.
If you need to send a small report to a colleague who will open it in a spreadsheet, CSV is convenient. The recipient does not need a specialised data-processing engine just to inspect the contents.
Many applications offer CSV exports because it is a widely supported interchange format. It is often a practical option when compatibility matters more than analytical efficiency.
If you have a few thousand records and a simple analysis, converting the file to Parquet may not provide enough benefit to justify an additional step.
When something looks wrong, opening a CSV file and inspecting a few lines can be faster than using a specialised viewer. Just remember that spreadsheet applications may reinterpret dates, long numbers or identifiers when opening or saving files.
Parquet becomes particularly useful when data is produced for repeated analytical processing rather than direct human editing.
If you routinely query millions of rows, especially when each query uses only a handful of columns, Parquet is worth considering.
Data lakes often store data in object storage and process it using engines such as Apache Spark, DuckDB and other analytical tools. Parquet is widely supported across this ecosystem.
If the same dataset is queried repeatedly, the reduced reading requirements and typed representation can be useful. The gains depend on the query engine and the way the files are organised.
Parquet is a practical format for intermediate datasets that move between ingestion, cleaning, transformation and analytical stages. It preserves schema information and is supported by many Python and distributed data-processing tools.
You do not need to build a complicated pipeline to experiment with the difference. DuckDB can query both CSV and Parquet files using SQL.
To read a CSV file:
SELECT * FROM read_csv('sales.csv');
To read a Parquet file:
SELECT * FROM read_parquet('sales.parquet');
Now suppose you want to calculate total revenue by region from the Parquet file:
SELECT region, SUM(revenue) AS total_revenue
FROM read_parquet('sales.parquet')
GROUP BY region
ORDER BY total_revenue DESC;
The SQL is familiar. The difference lies in how the source data is stored and how the query engine can access it.
DuckDB can also write query results to Parquet, which makes it useful for converting raw CSV exports into a format better suited to repeated analytics.
One practical workflow is to receive raw CSV files, validate their columns and types, clean the data, and export the result to Parquet for downstream analysis. Keep the original CSV when it serves as your raw source or interchange format.
Parquet is often a better fit for large analytical workloads because it supports column-oriented storage, compression and selective column reading. CSV remains useful for data exchange, manual inspection and simpler workflows.
Parquet is not a plain-text format like CSV, so it generally requires a compatible application, connector or conversion step before you can work with it in a spreadsheet.
Yes. Parquet stores schema information and typed column metadata. This helps compatible readers interpret values without relying entirely on inference from text.
Yes. Python tools such as pandas support both formats. Parquet workflows commonly use a compatible engine such as PyArrow or another supported Parquet implementation.
No. Convert files when the benefits justify the work, especially when datasets are large or queried repeatedly. CSV may be more convenient for small files, sharing and manual inspection.
CSV and Parquet are not really fighting for the same job.
CSV is excellent when you need a simple, readable format that works almost everywhere. Parquet is designed for analytical workloads where storage efficiency and selective data access matter.
If you are sharing a small dataset, CSV is often enough. If you are building a data pipeline that repeatedly scans large datasets, Parquet is usually worth evaluating.
And you do not have to choose only one. A practical workflow can use CSV for incoming exports and human-friendly sharing, then convert validated data to Parquet for analytics.
The best file format is the one that fits how your data will actually be used.
Anas Khan
Technology and data science content writer, researcher, and educator.