Comparison of Semantic Web Data Performance Using Virtual and Cloud Services

The term “big data” refers to the rapid development and availability of incredibly massive data collections that can be computationally processed to reveal patterns, trends, and relationships. The term “big data” refers to a compilation of vast amounts of data produced by various sources. Data collection and retrieval are becoming more complicated as the volume of data grows. Big Data Techniques refers to a modern wave of technologies and architectures that are designed to derive information from massive amounts of data in a number of formats. Hadoop uses a massively parallel computing strategy to process data that is scattered through a commodity cluster instead of being processed sequentially. The Hadoop MapReduce Model's mapper and reducer phases aid in the processing of vast volumes of data in a distributed environment, which has an impact on efficiency. Each portion of the data can be separated into smaller bits and stored on a different node within the cluster. With various data loading sizes, nodes were organised using the Amazon web service and the virtual environment. When compared to both frameworks, Hadoop designed in the cloud plays a critical role in data warehousing and clustering to achieve the highest results. As a result, the semantic web data are divided into pre- processing and clustering in this analysis to boost accuracy.