Understanding the Parquet File Format: A Comprehensive Guide | by Siladitya Ghosh | Medium

What is Parquet?

Apache Parquet is a columnar storage file format designed for big data processing frameworks like Hadoop, Spark, and Drill. It's optimized for efficient data compression and encoding, leading to performance improvements over row-based storage.

Key Features

• Columnar Storage: Data is stored by columns, not rows, making it efficient for analytical queries accessing subsets of columns.
• Efficient Compression: Columnar format allows for better compression due to similar data types within a column.
• Encoding Schemes: Supports RLE, Dictionary Encoding, and Delta Encoding for enhanced compression and performance.
• Schema Evolution: Allows adding or removing fields without impacting existing data.
• Compatibility: Works with various data processing frameworks like Apache Hive, Impala, Drill, and Spark.

Advantages

Parquet's columnar storage significantly improves query performance, especially in read-heavy analytical workloads, by reducing the amount of data that needs to be processed.