What are the features of Parquet schemas and why is it efficient for ML?

Parquet is a columnar data storage format optimized for analytical tasks and machine learning. Its schema features include:

• Columnar storage: data is stored by columns, allowing efficient reading of only the necessary features, reducing data transfer volume.
• Schema with data types: Parquet stores metadata about data types and structure, simplifying validation and processing.
• Compression and encoding: optimal compression and encoding methods are applied to each column, reducing file size and speeding up I/O.
• Support for nested structures: Parquet can store complex types such as arrays and structures, useful for flexible data.

Efficiency for ML is due to the fact that training models often requires only certain features, and Parquet allows quick and economical extraction of these. Additionally, reducing data volume decreases load and processing time, which is critical when working with large datasets.