Spark ML (MLlib) is Apache Spark's scalable machine learning library. It is designed to operate natively on distributed Spark DataFrames. It provides a comprehensive set of feature engineering tools, such as VectorAssembler, StandardScaler, and OneHotEncoder, which are implemented as distributed algorithms. These tools process data in parallel across a cluster without requiring the user to write custom logic in a User-Defined Function (UDF) or use the pandas Function API. This makes Spark ML the ideal choice for performing large-scale, distributed feature engineering directly within a Spark-based machine learning pipeline.

A. Keras is a deep learning framework, not a general-purpose tool for distributed feature engineering on tabular data.

B. pandas is a single-node library and cannot perform distributed computation on its own.

C. PyTorch is a deep learning framework focused on tensor computation, not a native distributed feature engineering library for DataFrames.

E. Scikit-learn is a single-node library; using it at a distributed scale would require wrappers like pandas UDFs, which the question explicitly excludes.

1. Databricks Official Documentation

"Feature engineering and featurization": This document states

"You can use Spark ML to scale feature engineering... Spark ML provides a broad set of feature transformers that you can apply to your data." It then lists numerous built-in transformers like StandardScaler and VectorAssembler that operate on Spark DataFrames. (See section: "Feature engineering with MLlib").

2. Databricks Official Documentation

"Single-node and distributed training": This page contrasts single-node libraries with distributed ones. It clarifies

"For big data

you can use Spark’s machine learning library

MLlib... MLlib is a distributed library and is the only ML library that comes pre-installed on Databricks Runtimes." This highlights that Spark ML is the native distributed tool

unlike Scikit-learn. (See section: "Distributed training").

3. Karau

H.

Konwinski

A.

Wendell

P.

& Zaharia

M. (2015). Learning Spark: Lightning-Fast Big Data Analysis. O'Reilly Media

Inc. Chapter 6

"Machine Learning with MLlib

" describes the library's architecture

stating

"MLlib is Spark’s library of machine learning algorithms... The feature extraction and transformation utilities in MLlib help you create features from your raw data." The chapter details transformers that are inherently distributed. (See Chapter 6

"Feature Extraction and Transformation").