The Enhanced Performance Optimization of Combining Online and Offline Data for Integrated Big Data Analytics

Integrated ample facts analytics, the aggregate of online and offline information, is a more and more popular form of massive information analytics. It entails mixing information sources, including information warehouses, based databases, and legacy structures, with new sources of unstructured records, including social media, sensors, and net of factors (loT) facts. Its use allows for a complete view of client conduct, market tendencies, capability possibilities, and advanced decision-making. This study explores more advantageous performance optimization for integrated massive statistics analytics. It examines exceptional ready-to-use techniques for combining datasets, such as parallel processing, allotted computing, statistics engineering tools, and Hadoop-based equipment. It also analyzes the overheads incurred when using those tactics: reminiscence and storage constraints, performance bottlenecks, and statistics incompatibility problems. Facts scalability, robustness, and safety also are discussed. Sooner or later, the paper gives a comprehensive overview of research progress and future instructions on more substantial overall performance optimization for integrated massive information analytics.