Parquet metadata lives in the file footer: the schema, row group layout and per-column min/max statistics that let query engines skip data they do not need.
How to read and write Parquet files in Python with PyArrow, FastParquet, and Pandas, including column selection, compression, and partitioned datasets.
Why Parquet is the default file format for data lakes, how it underpins Iceberg, Delta Lake, and Hudi, and best practices for partitioning and pushdown.
Tune Apache Parquet for speed and cost: row group and file sizes, partitioning, Snappy vs ZSTD compression, encodings, sorting, and predicate pushdown.
