Efficient Data Processing Pipelines: Integrating Machine Learning with Big Data Frameworks

Abstract

Efficient data processing pipelines are crucial for handling the vast volumes of data generated in modern digital ecosystems. By integrating machine learning (ML) with big data frameworks, organizations can extract actionable insights, optimize decision-making, and enhance operational efficiency. This paper explores the design and implementation of data processing pipelines that seamlessly combine ML algorithms with big data platforms such as Apache Spark, Hadoop, and TensorFlow. We discuss the challenges of data ingestion, transformation, model training, and deployment in large-scale environments. Emphasis is placed on the role of distributed computing, parallelism, and real-time analytics in improving performance and scalability. Through a detailed examination of advanced pipeline architectures and best practices, we highlight how integrating ML with big data frameworks accelerates data-driven innovation and ensures robust, scalable, and adaptive data systems.