Machine Learning Using Cassandra as a Data Source: The Importance of Cassandra's Frozen Collections in Training and Retraining Models

Abstract

This paper explores the integration of Apache Cassandra as a data source for machine learning (ML) applications, emphasizing the role of Cassandra's frozen collections in model training and retraining. The study highlights how Cassandra's distributed and scalable architecture enables efficient storage and retrieval of large, diverse datasets essential for machine learning tasks. A key focus is placed on the functionality of frozen collections within Cassandra, which allow for compact storage of complex data structures like lists, sets, and maps. By using these frozen collections, machine learning models can be trained and retrained more effectively, improving data consistency, performance, and scalability. The paper also presents case studies and experiments demonstrating how leveraging frozen collections can optimize the machine learning pipeline, reducing latency and enhancing real-time model updates.