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: It is supported by nearly all major data tools, including Apache Spark , Presto , and Pandas/PyArrow .

: Its columnar design allows for "column pruning" (reading only required columns) and "predicate pushdown" (filtering data at the storage level), which significantly speeds up data processing. PARQUET Free Download

: Unlike CSV, it can handle nested data structures like arrays and maps natively. Reading and Writing Parquet Files in Python - DEV Community : It is supported by nearly all major

: Parquet supports advanced compression techniques (like Snappy, Gzip, or Zstd) that reduce disk space usage and lower cloud storage costs. including Apache Spark