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APACHE PARQUET
Columnar storage format for analytics and ML
Apache-2.0
ABOUT
Storing large ML datasets in row-oriented formats like CSV or JSON wastes storage and slows down read-heavy workloads because queries scan entire rows even when only a few columns are needed. Apache Parquet's columnar layout, predicate pushdown, and advanced compression reduce storage footprint by up to 75% while enabling scans that are orders of magnitude faster for analytical queries and feature engineering.
INSTALL
pip install pyarrowINTEGRATION GUIDE
1. Store and query large-scale training datasets with efficient column pruning for faster data loading into ML pipelines
2. Power feature stores and data lakes where columnar compression reduces storage costs while maintaining high read throughput
3. Enable efficient data exchange between distributed query engines like Spark, Trino, and DuckDB for ETL workflows
4. Archive model training logs, evaluation metrics, and experiment artifacts in a compressed, self-describing format
TAGS
data-storagecolumnaranalyticsbig-dataarrowcompression