: Parquet supports advanced compression techniques (like Snappy, Gzip, or Zstd) that reduce disk space usage and lower cloud storage costs.
: Unlike CSV, it can handle nested data structures like arrays and maps natively. Reading and Writing Parquet Files in Python - DEV Community PARQUET Free Download
: It is supported by nearly all major data tools, including Apache Spark , Presto , and Pandas/PyArrow . : Its columnar design allows for "column pruning"
: 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. Why Use Apache Parquet
Apache Parquet is a columnar storage format widely used in the Apache Hadoop ecosystem and modern data lakes. Unlike traditional row-based formats like CSV, Parquet organizes data by columns, making it up to 10–100x faster for analytical queries. Why Use Apache Parquet?