Big Data and Cloud Computing Integration: A Review of Scalable Information Retrieval Techniques

Abstract

This presents an extensive overview of the integration of big data with cloud computing, focusing on scalable information retrieval methodologies capable of managing the volume, velocity, and diversity of data, as the imperative to amalgamate big data technologies with cloud computing emerges from the rapid and complex expansion of data originating from diverse digital sources. The paper also explores fundamental architectural models such as distributed storage systems, parallel processing frameworks, and cloud-based service models for data analytics on a large scale how retrieval performance, fault tolerance, and resource utilization could be enhanced through the use of the technologies like Hadoop, MapReduce, Spark, and NoSQL databases. Additional topics covered in the article include security, scalability, data heterogeneity, latency, and cloud-based big data conditions. The comparative insights into different existing retrieval techniques have been offered to point out the advantages, limitations, and application domains. Cloud computing’s function in big data information retrieval and unanswered questions about how to build more effective, safe, and scalable retrieval systems are the results of their most recent study.