Improving Data Analysis Performance with Parquet

Stop using large .csv files for your data analysis.

Instead, consider using the parquet format. 📦

Parquet is an open-source file format designed for efficient and performant flat columnar storage. Compared to traditional row-based files like CSV, Parquet files use columnar storage, significantly benefiting analytics applications. 📊

Why Parquet?

• Efficiency and Speed: Parquet files provide efficient data compression and encoding schemes. This reduces storage needs and speeds up data retrieval. Reading data from Parquet files is significantly faster than reading from CSV, particularly for large datasets. 🚀

• Selective Loading: One of the most significant advantages of Parquet is the ability to read only specific columns from the file, not the entire dataset. This is particularly useful for large datasets where loading entire datasets into memory is unnecessary or impractical. 🎯

• Compatibility: Parquet is compatible with many data processing frameworks, making it a versatile choice for data storage. 🔄

** You can already use parquet if you are using pandas or polars!! **

What’s been your experience with parquet? Did you know about this file format? Share your thoughts in the comments! 💬