Parquet files organize data in columns, while CSV files organize data in rows.
Columnar storage allows much better compression so Parquet data files need less storage, 1 TB of CSV files can be converted into 100GB of parquet files – which can be a huge money saver when cloud storage is used. This also means that scanning parquet file is much faster than scanning CSV files – fewer data would be scanned and there is no need to load unneeded columns into memory and aggregations will run faster. Parquet files contain both data and metadata, information about data schema and structure. When you load the file, having metadata helps the querying tool define proper data types.
Click through for an example of when Parquet makes sense. It’s not the best format for everything—it’s a columnar file format, so writes are typically slower than row-store formats like CSV or Avro—but it and ORC are outstanding for analytical processing, not least because of the metadata these formats contain.