OPTIMIZING HADOOP CLUSTER PERFORMANCE: A COMPREHENSIVE FRAMEWORK FOR BIG DATA EFFICIENCY - Ramalingeshwar Sirigade, (INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND INFORMATION TECHNOLOGY (IJRCAIT), 2024)
Optimizing Hadoop performance is essential for maximizing the value of Big Data initiatives. This article provides practical tips and best practices for enhancing the efficiency of Hadoop clusters. Covering key areas such as resource allocation, job scheduling, and data processing optimization, the article draws on real-world experience to offer actionable advice. It discusses techniques for fine-tuning system configurations, leveraging advanced features, and addressing common performance bottlenecks. The article concludes with case studies demonstrating successful Hadoop optimization in large-scale environments, offering valuable insights and lessons learned. By implementing these strategies, Big Data professionals can significantly improve their Hadoop environments' scalability, reliability, and speed, ensuring they meet the demands of increasingly complex data workloads.