A Survey of Data Handling Techniques in NextGeneration Database Systems
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Abstract
The rapid expansion of digital technologies such as cloud computing, Internet of Things (IoT), artificial intelligence, and
big-data applications has significantly increased the volume, variety, and velocity of data generated across modern systems.
Traditional relational database management systems (RDBMS), while effective for structured and transactional data, often
encounter limitations in handling large-scale, heterogeneous, and real-time data environments. Consequently, next-generation
database systems, including NoSQL, NewSQL, graph, distributed, and cloud-native databases, have emerged to address these
challenges through scalable, flexible, and high-performance architectures. This review paper surveys major data handling techniques
adopted in next-generation database systems, emphasizing data storage models, organization strategies, query processing,
optimization mechanisms, and partitioning techniques. The study further discusses advanced challenges associated with scalability,
performance, security, privacy, and governance in distributed and cloud-based environments. In addition, emerging technologies
such as artificial intelligence-driven database management, serverless databases, edge computing, blockchain integration, and multi
cloud optimization are examined as transformative developments shaping future database ecosystems. A comprehensive literature
review summarizes recent contributions, identifies limitations, and highlights research opportunities. The findings indicate that
intelligent and adaptive data handling techniques are essential for ensuring efficient, secure, and scalable database management in
modern data-intensive applications.
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