Summary

In this chapter, we showed how parallel computing helps when single-core pandas and scikit-learn become slow or run out of memory. We introduced Dask as both a parallel execution engine and a set of familiar collections, and we explained how the scheduler, workers, and dashboard work together to execute a task graph created by lazy operations. We then applied this foundation through Dask Arrays for numeric workloads, Dask DataFrames for partitioned tabular processing, and Dask Bags for semi-structured data such as JSON Lines.

Next, we demonstrated practical patterns for parallel data loading, preprocessing, and feature preparation at scale, followed by machine learning workflows that either parallelize scikit-learn steps or use dask-ml to train on Dask-backed data. We closed by comparing Dask with Modin and Ray. Modin aims for minimal changes to pandas code, while Ray provides a general runtime for orchestrating many tasks and models with more explicit control over execution...