Article Highlights 7 Python Libraries for Large-Scale Data Processing

1 articles · Updated · KDnuggets · May 26

Seven tools lead the article’s list for handling data beyond pandas’ limits: PySpark, Dask, Polars, Ray, Vaex, Kafka Python clients, and DuckDB.
Large datasets, distributed machine learning, and real-time event streams drive the need for these libraries, which target workloads that exceed single-machine memory or require cluster-scale execution.
PySpark, Dask, Ray, and Kafka focus on distributed or streaming pipelines, while Polars, Vaex, and DuckDB emphasize faster local analytics, lazy execution, and efficient processing of large files.
The article frames the lineup as a practical guide for modern data workflows spanning ETL, SQL analytics, model training, and production pipelines, and points readers to tutorials and project ideas for each tool.

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