Parquet and Columnar Formats for ML Data

Parquet is an open column-oriented file format for analytical data. A file contains row groups; each row group has a column chunk for each field, and column chunks contain pages. This layout lets a reader fetch selected columns without necessarily reading all fields in each row. Data pages can use encodings and compression appropriate to the column’s values. Metadata can also help some engines avoid work. Column statistics such as minimum and maximum values, dictionaries, and optional page indexes can allow readers to skip data that cannot match a filter. These optimizations require compatible readers and useful data organization. For instance, page indexes are optional and not all tools use every feature the same way. For machine-learning workflows, Parquet can be useful for wide tabular datasets where training or feature queries read a subset of columns. It supports typed values and nested structures. CSV may be simpler to inspect, but it often repeats text and lacks the same typed metadata. Neither format alone defines a complete table catalog, transaction protocol, or training-dataset version history. Columnar formats may be less suitable for workloads dominated by tiny random row lookups or frequent single-row updates. Row-group size, partitioning, compression codec, sort order, and schema evolution practices affect performance. Benchmark representative reads and writes on the actual engine and storage system, and preserve schema and dataset metadata separately where needed.