Overcoming Big Data Challenges: Scalability, Quality, and Privacy in AI-Integrated Systems

Abstract

The swift progression of sensor networks, IoT devices, and Big Data technology has changed the way data is being managed in numerous sectors including government agencies, healthcare and smart cities. For the emergent technological advancement, it is no longer sufficient to merely acquire data. The real value of big data lies in using AI to analyze data instantly and generate useful insights. When AI is used in big data technology, it creates concerns such as data scalability, data quality, interpretability, and global data privacy regulations. To address such issues, technologies like edge computing, federated learning, and zero-trust architecture are being cast-off. By means of an innovative synthesis of big data architectural development, ethical data practices, and AI integration, this paper offers a unified framework that conforms to emerging. By connecting these dimensions, the research offers a forward-looking view on creating intelligent, adaptive, and regulation-compliant data ecosystems.