NoSQL Database Integration with Scalable Machine Learning Pipelines

NoSQL Database Integration with Scalable Machine Learning Pipelines

Tilak Kumar1, Surya2, Kushagra Deep3

1,2,3,4 Department of Computer Science and engineering

Presidency university, Bengaluru

Abstract

The increasing requirement to process large amounts of diverse and high-velocity data has made the integration of NoSQL databases with scalable machine learning (ML) pipelines an important research topic. With a focus on scalability, flexibility, and real-time processing, this article examines the architectural and technical obstacles to a smooth integration between NoSQL databases and ML workflows. We look into the suitability of different NoSQL database models (such as document-based, columnar, and graph-based) for handling and storing the variety of data types needed for machine learning training and inference activities. Alongside cutting-edge technologies like distributed computing frameworks and containerized machine learning platforms, the study also looks at important design factors including parallel processing, data retrieval optimization, and large-scale data preprocessing.