The rapid growth of enterprise data generated through digital platforms, social media, cloud applications, and Internet of Things (IoT) devices has created significant challenges for organizations in storing, processing, and analyzing massive datasets. Traditional database systems are often incapable of handling the volume, velocity, and variety of big data efficiently. Hadoop-based distributed frameworks integrated with cloud computing technologies have emerged as scalable and cost-effective solutions for enterprise business analytics. This study explores scalable cloud-enabled Hadoop-based distributed frameworks and their role in enhancing enterprise data analytics capabilities. The framework utilizes distributed storage through Hadoop Distributed File System (HDFS), parallel processing with MapReduce, and cloud infrastructure for elastic resource allocation. The integration of cloud services with Hadoop improves scalability, fault tolerance, processing speed, and accessibility while reducing infrastructure costs. The research further examines the implementation methodologies, architectural components, and practical applications of Hadoop ecosystems in business intelligence and decision-making processes. Additionally, the study evaluates the benefits and limitations of cloud-enabled Hadoop frameworks in enterprise environments. Findings indicate that organizations adopting these frameworks achieve improved operational efficiency, real-time analytics, and competitive advantages. However, challenges related to security, data privacy, and system complexity continue to require advanced research and technological improvements for sustainable enterprise analytics solutions.
