The proliferation of big data and the diverse, distributed data sources that accompany it have given rise to the need for efficient and effective federated query processing in the context of big data integration. This paper explores the approaches and prospects of federated query processing, shedding light on the challenges, techniques, and future directions in this domain. Federated query processing is the art and science of seamlessly querying and retrieving data from heterogeneous, distributed data sources while maintaining performance, scalability, and data integrity. The challenges posed by the heterogeneity of data sources, varying data formats, and the distributed nature of big data have necessitated innovative approaches to enable effective query processing. This paper delves into various techniques and methodologies employed in federated query processing. It discusses data virtualization, query optimization, query rewrite, metadata management, and semantic integration as essential components of successful query federation. Additionally, it addresses the role of query federation middleware in orchestrating queries across distributed data sources. In conclusion, federated query processing plays a pivotal role in addressing the challenges of big data integration. The prospects of this approach are promising, enabling organizations to harness the full potential of their distributed data assets. As technology advances, federated query processing is poised to become an indispensable tool for organizations seeking to extract valuable insights from their growing repositories of big data.
Keywords: Big Data, Big Data Integration, Distributed Data Sources, Federated Query Processing
