NoSQL Database Integration with Scalable Machine Learning Pipelines

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.