Parquet is a column-based file format that stores data more efficiently than spreadsheets or traditional databases

A Parquet file is a way of organizing and storing data that breaks information into columns instead of rows. Instead of saving every piece of data about one person in a line (name, age, address, phone), Parquet saves all the names together, all the ages together, all the addresses together. This arrangement lets computers read and process only the columns they need, which saves time and storage space.

Parquet files are used by data analysts, engineers, and companies that work with large datasets. You will not encounter them in everyday consumer software like Microsoft Excel or Google Sheets, but they power the data systems behind the services you use — recommendation engines, analytics dashboards, and cloud storage platforms all rely on formats like Parquet.

The format was created by Apache, an open-source software organization, and is now supported by most major data processing tools including Python, Java, and cloud platforms like Amazon Web Services and Google Cloud.

Key Takeaways

  • Parquet stores data by column rather than by row, which means a computer can read only the information it needs instead of loading entire records.
  • Files in Parquet format take up less disk space and process faster than the same data stored in CSV or JSON formats.
  • Parquet is designed for large datasets and analytics work, not for everyday spreadsheet tasks or small files.
  • Most data analysis tools and cloud platforms support Parquet, making it a standard format for moving data between different systems.

How Parquet stores data differently from spreadsheets

A spreadsheet like Excel stores data row by row. If you have a table with 1 million customers and 50 columns of information about each one, the spreadsheet reads and writes all 50 columns for every customer, even if you only need the email address column. This wastes processing power and memory.

Parquet stores the same data column by column. All 1 million email addresses sit together in memory, all 1 million names sit together, and so on. When you ask for only the email addresses, the computer reads just that column without touching the others. For large datasets, this difference is enormous — queries run 10 to 100 times faster, and the file itself takes up 50 to 80 percent less space.

Parquet also compresses data automatically. Numbers, text, and dates are squeezed down using algorithms that recognize patterns. A column of dates, for example, compresses much more efficiently when all the dates are stored together than when they are scattered throughout a spreadsheet.

When companies and analysts use Parquet files

Data teams use Parquet when they are working with datasets that contain millions or billions of rows. A company analyzing customer behavior, a research lab processing sensor data, or a streaming service tracking viewing habits will all convert their data to Parquet format before analysis begins.

Parquet is also the standard format for moving data between different tools and platforms. If you need to send data from a database to a Python script to a cloud analytics service, Parquet is often the bridge — it is supported everywhere and loses no information in translation.

For small datasets or everyday work, Parquet adds unnecessary complexity. A spreadsheet with 10,000 rows and 5 columns does not benefit from Parquet's advantages. The format is built for scale, not convenience.

Parquet compared to CSV and JSON

CSV (comma-separated values) is the most common format for sharing data. It is human-readable, opens in any spreadsheet, and requires no special software. But CSV stores data row by row, compresses poorly, and forces you to read entire rows even when you need only one column. A CSV file with 1 million rows of customer data might be 5 gigabytes; the same data in Parquet might be 500 megabytes.

JSON is a text-based format that stores data as nested objects and is popular for web applications and APIs. Like CSV, JSON is row-oriented and human-readable, but it is slower to process and takes up more space than Parquet. JSON is better for small, structured data moving between web services; Parquet is better for large analytical datasets.

The trade-off is readability. You can open a CSV file in Notepad and see the data. A Parquet file is binary — it looks like gibberish if you open it in a text editor. You need a tool designed to read Parquet to see what is inside. For data teams, this trade-off is worth it. For sharing data with non-technical people, CSV is still the standard.

How to work with Parquet files

If you work with data in Python, the pandas library reads and writes Parquet files in two lines of code. The command df.to_parquet('file.parquet') converts a spreadsheet into Parquet format; pd.read_parquet('file.parquet') loads it back. Other languages like R, Java, and C++ have similar tools.

Cloud platforms like Amazon S3, Google Cloud Storage, and Microsoft Azure all store and process Parquet files natively. If your data lives in the cloud, Parquet is often the default format because it integrates seamlessly with analytics tools like Spark, Presto, and BigQuery.

You do not need to understand Parquet's internal structure to use it. The tools handle the conversion automatically. Your job is to know when to use it — when you have a large dataset and need speed and storage efficiency, Parquet is the right choice.

Why Parquet became the standard for big data

Parquet was designed to solve a real problem: older formats like CSV and JSON were too slow and too large for the massive datasets that companies were collecting. As data grew from gigabytes to terabytes, the inefficiency of row-based storage became a bottleneck.

Parquet is also language-agnostic and open-source, which means any company or tool can support it without paying licensing fees. This openness led to widespread adoption across the data industry. Today, if you are working with analytics, machine learning, or data engineering, Parquet is almost certainly part of your workflow.

The format also supports nested and complex data structures, which means it can handle data that does not fit neatly into a simple table. This flexibility, combined with its efficiency, made Parquet the default choice for modern data platforms.

Frequently Asked Questions

Can I open a Parquet file in Excel or Google Sheets?

Not directly. Excel and Google Sheets do not recognize Parquet format. You need to convert the file to CSV or another text-based format first, or use a specialized tool. Most data teams use Python, R, or a cloud platform to work with Parquet rather than trying to open it in a spreadsheet.

Is Parquet the same as Avro or ORC?

No. Avro and ORC are different column-based formats designed for similar purposes. Parquet is the most widely supported across different tools and platforms, which is why it has become the industry standard. Avro is often used for streaming data, and ORC is optimized for Hive, a specific data warehouse tool.

Do I need to convert my data to Parquet?

Only if you are working with large datasets or moving data between different systems. For small spreadsheets or everyday work, CSV or Excel format is fine. Parquet adds complexity that is only worth it when you are dealing with millions of rows or need to optimize storage and processing speed.

How much space does Parquet actually save?

The savings depend on your data. Text and dates compress well and might shrink to 10 to 20 percent of their original CSV size. Numbers compress less. On average, expect Parquet files to be 50 to 80 percent smaller than the same data in CSV format, plus faster read times.

What happens if I need to share a Parquet file with someone who does not know about it?

Convert it to CSV before sharing. CSV is universal and opens anywhere. If the file is very large, warn the recipient that it will be much bigger in CSV format and may be slow to open in a spreadsheet. For large files, it is often better to share a summary or a subset of the data instead.