What is the Parquet file format? How does it differ from other file formats and what are its advantages?

Parquet is an open‑source, columnar storage format designed for efficient data processing on big‑data platforms such as Hadoop, Spark, and Hive. Unlike row‑oriented formats (e.g., CSV, JSON), Parquet stores data by column, which enables better compression and encoding because values in a column are usually of the same type and have similar patterns. This columnar layout also allows query engines to read only the columns needed for a query, reducing I/O. Parquet supports schema evolution, meaning new columns can be added without rewriting existing data. It also stores metadata (like min/max values per column) that enables predicate pushdown, allowing engines to skip entire blocks that do not satisfy a filter. Compared to other columnar formats like ORC, Parquet is more widely supported across ecosystems and offers similar compression and performance characteristics. Overall, Parquet reduces storage footprint, speeds up query execution, and integrates seamlessly with many data processing frameworks.