A parquet file is a way to store data in columns instead of rows, which makes it faster to search and uses less disk space than older formats

Most spreadsheets and databases store information in rows — think of a table where each row is one person's record, with columns for name, age, email. A parquet file flips that around and stores all the names together, all the ages together, all the emails together. This matters because when you want to find everyone over 30, the computer only has to read the age column instead of reading every single row.

Parquet files are used by data analysts, engineers, and anyone working with large datasets in tools like Python, Apache Spark, or cloud databases. You will not encounter them in everyday work unless you are downloading data from a company's analytics team or working with big datasets yourself. But if you do, understanding what they are prevents confusion about file types and why your data looks different when you open it.

Key Takeaways

  • Parquet files store data by column rather than by row, which lets computers find specific information much faster.
  • A parquet file takes up less storage space than the same data in CSV or Excel format because it compresses repetitive information.
  • You cannot open a parquet file in Excel or a text editor — you need specialized tools like Python, R, or a database program.
  • Data teams use parquet files when they are working with millions or billions of rows, where speed and storage space matter.

How parquet files store data differently from CSV and Excel

A CSV file (comma-separated values) or an Excel spreadsheet stores data row by row. If you have 1 million customer records with 50 columns each, the file writes out all 50 pieces of information for customer 1, then all 50 for customer 2, and so on. A parquet file instead writes out all 1 million values in the first column, then all 1 million values in the second column.

This matters for two reasons. First, when you search for something — like "all customers who spent more than $500" — the computer only reads the spending column instead of reading all 50 columns for every row. Second, columns often contain the same or similar values repeated many times. If you have a column for state and most customers are in California, a parquet file can compress that repetition. The same data in CSV format cannot compress it the same way, so the file is much larger.

A parquet file also stores information about what type of data is in each column — whether it is text, a number, a date, or something else. This helps prevent errors when different programs read the file, because they all know exactly what they are looking at.

Why data teams choose parquet over other formats

For small datasets — a few thousand rows — the difference between parquet and CSV is not worth the extra complexity. You can open a CSV in Excel, email it, and work with it easily. But when you have millions or billions of rows, parquet becomes practical because it saves money and time.

Storing data costs money in the cloud. If a parquet file is one-tenth the size of the same data in CSV, you pay one-tenth as much to store it. Reading data also costs money and time — if your query only needs one column out of fifty, parquet lets you read just that column instead of the whole file. For a company running thousands of queries a day, that adds up to real savings.

Parquet also works well with the tools data teams already use. Apache Spark, which processes huge datasets across many computers, reads parquet files natively. Python libraries like Pandas and Polars read parquet files directly. Cloud databases like Google BigQuery and Amazon Athena expect parquet format. Using parquet means the data flows smoothly between these tools without conversion steps.

What you need to open and read a parquet file

You cannot double-click a parquet file and open it in Excel or Notepad. The file is stored in a binary format, which means it is optimized for computers to read, not for humans to read as text. If you try to open it in a text editor, you will see gibberish.

To work with a parquet file, you need one of these tools: Python with a library like Pandas or Polars, R with the Arrow package, a database program like DuckDB or SQLite, or a cloud service like Google BigQuery or Snowflake. If someone sends you a parquet file and you do not have these tools, the easiest path is usually to ask them to convert it to CSV first, or to use a free online converter that turns parquet into CSV so you can open it in Excel.

Some data visualization tools like Tableau or Power BI can read parquet files directly, which is useful if you are building a dashboard. But for straightforward viewing or editing, parquet is not the right format.

The difference between parquet and other columnar formats

Parquet is not the only columnar format — there is also ORC (Optimized Row Columnar), which does something similar. ORC is used more often in Hadoop environments, while parquet is more common in cloud data warehouses and with Python tools. For most purposes, they solve the same problem and the choice between them depends on what tools your team already uses.

There is also Avro, which is a row-based format but still compresses well and includes schema information. Avro is often used for streaming data or moving data between systems, while parquet is better for storing data that will be queried many times. Again, the choice depends on what you are trying to do.

If you are working with a data team and they mention one of these formats, the key thing to know is that they are all trying to solve the same problem: store large amounts of data efficiently and make it fast to search. Parquet just happens to be the most popular choice right now for analytics work.

When you might encounter a parquet file in your own work

If you work in marketing, product, or operations at a company with a data team, you might receive a parquet file when you request a dataset. Instead of getting a CSV file, the data team sends you parquet because it is smaller and faster to generate. If this happens, ask them to convert it to CSV or Excel, or ask them to show you how to open it in the tool you already use.

If you are learning Python or data analysis, you will probably work with parquet files in tutorials and practice projects. Most data science courses teach you to read parquet files because it is a skill you will use in real jobs. The process is straightforward — usually just one line of code in Python — but you do need to know it is a different format from CSV.

If you are downloading public datasets from sources like Kaggle or government data portals, some are offered in parquet format because it saves them storage space. In those cases, the read page usually explains what format the data is in and what tools you need.

Frequently Asked Questions

Can I convert a parquet file to CSV or Excel?

Yes. If you have Python installed, you can convert it in a few lines of code. If you do not have Python, you can use a free online converter tool — search "parquet to CSV converter" and upload your file. Some data visualization tools like Tableau can also export parquet data to CSV.

Why is parquet better than just using a database?

Parquet files are simpler to share and move around than a database. You can email a parquet file, upload it to cloud storage, or move it between systems without setting up a database server. Databases are better if many people need to access the same data at the same time, but for sharing datasets or archiving data, parquet is lighter and cheaper.

Is parquet only for big data?

Technically no, but practically yes. For datasets under a few million rows, CSV or Excel is simpler and faster to work with. Parquet shines when you have huge datasets where the file size and query speed actually matter. Using parquet on small data just adds unnecessary complexity.

Do I need to learn parquet to work with data?

Not necessarily. If you work with data in Excel or Google Sheets, you probably will not encounter parquet. If you move into data analysis, Python, or work closely with a data team, you will eventually need to know what it is and how to open it. But it is not hard to learn — most tutorials cover it in a few minutes.