Cloud-Based Big Data Analytics with Machine Learning Integration

Abstract

The exponential growth in data generation has rendered traditional data processing techniques insufficient for modern analytical demands. Cloud-based big data analytics has emerged as a powerful solution to address the storage, processing, and analysis of vast and complex datasets. When integrated with machine learning (ML), cloud computing platforms offer scalable, efficient, and intelligent data analytics capabilities. This paper explores the synergistic relationship between cloud computing, big data analytics, and ML, outlining how their integration enhances real-time decision-making, predictive modeling, and operational efficiency across diverse sectors such as healthcare, finance, and IoT The study reviews the architecture of cloud platforms used for big data analytics, including Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), focusing on how they incorporate ML tools like TensorFlow, PyTorch, and Apache Spark ML lib. It also highlights various ML algorithms frequently employed in cloud based analytics—such as decision trees, support vector machines, and deep learning networks—examining their scalability and performance. Furthermore, the methodology details a simulated case study using cloud infrastructure to analyze large datasets using ML models, demonstrating how latency, cost-efficiency, and accuracy can be optimized through the integration. Challenges such as data privacy, security, interoperability, and the need for skilled professionals are also addressed, along with potential solutions. This paper contributes to the understanding of how cloud-based ML analytics transforms raw big data into actionable insights. It provides a practical framework for researchers and industry professionals to harness the power of cloud computing and machine learning in managing and extracting value from big data.