Big Data Integration and Interoperability: Overcoming Barriers to Comprehensive Insights

Abstract

Big Data integration and interoperability are critical challenges in the modern data landscape, where diverse and voluminous data sources must be unified to generate comprehensive insights. This paper explores the barriers to effective data integration, including heterogeneous data formats, incompatible systems, and varying data governance policies. By analyzing current methodologies, tools, and frameworks, the paper identifies key strategies for overcoming these challenges. The proposed solutions emphasize the importance of standardization, semantic interoperability, and the use of advanced technologies such as machine learning and artificial intelligence to facilitate seamless data integration. The findings highlight how overcoming these barriers can unlock the full potential of Big Data, enabling organizations to derive more accurate, timely, and actionable insights.