In Python, Parquet is a columnar storage file format that is designed for efficient data storage and processing. It is optimized for use with big data processing frameworks, such as Apache Hadoop and Apache Spark, but can also be used in standalone Python applications.
The Parquet format offers several advantages over traditional row-based file formats, such as CSV or JSON, especially when working with large datasets:
Columnar storage: Parquet stores data column-wise rather than row-wise. This columnar organization allows for more efficient compression and encoding, as similar data values are stored together, reducing storage space and improving query performance.
Compression: Parquet supports various compression algorithms, such as Snappy, Gzip, and LZO. Compression helps to reduce the size of the data files, resulting in faster I/O operations and lower storage requirements.
Predicate pushdown: Parquet supports predicate pushdown, which means that when executing queries, it can skip reading entire columns or row groups based on the query predicates. This capability improves query performance by minimizing disk I/O.
Schema evolution: Parquet files can handle schema evolution, allowing for flexibility in adding, modifying, or deleting columns from the dataset without the need to rewrite the entire dataset.
To work with Parquet files in Python, you can use libraries like pyarrow or pandas that provide convenient APIs for reading and writing Parquet data. These libraries offer methods for converting data between Parquet files and other data structures like DataFrames, enabling seamless integration with existing Python data processing workflows.
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