Understand Parquet file format and how Apache Spark makes the best of it

Parquet is a hybrid columnar and row-group storage format that combines the benefits of both approaches. The format organizes data into row groups containing column chunks, which are further divided into pages with metadata for efficient querying. Apache Spark leverages Parquet's structure through column pruning, predicate pushdown, vectorized reading, partition pruning, and compression optimizations. These features enable Spark to skip unnecessary data, read only required columns, and process data in batches, resulting in significant performance improvements for big data analytics workloads.