Modern Database Technologies for Intelligent Data Management

Ernest Teniente

Modern Database Technologies for Intelligent Data Management

Author(s)	Prof. Ernest Teniente
Country	Spain
Abstract	The rapid expansion of digital information has transformed database management from conventional data storage toward intelligent, distributed, scalable, and continuously adaptive information-management environments. Organizations increasingly generate large volumes of structured, semi-structured, and unstructured data through enterprise applications, cloud platforms, mobile systems, Internet of Things devices, social platforms, transaction systems, and intelligent services. Traditional database architectures may face limitations when managing high-volume, heterogeneous, rapidly changing, and geographically distributed information. Modern database technologies provide advanced capabilities through cloud databases, distributed databases, NoSQL systems, NewSQL architectures, graph databases, multimodel platforms, in-memory processing, data virtualization, database automation, and artificial intelligence-assisted management. This study examines modern database technologies for intelligent data management and evaluates their contribution to data accessibility, scalability, performance, consistency, security, integration, analytics, automation, and decision support. A qualitative descriptive research design based on secondary academic literature was adopted to examine contemporary database approaches and their application to intelligent data environments. The analysis indicates that modern database technologies enable organizations to select specialized storage and processing mechanisms according to data characteristics and application requirements. Distributed databases can support large-scale workloads, cloud databases provide elastic infrastructure, NoSQL systems facilitate flexible data structures, graph databases support relationship-oriented analysis, and in-memory technologies can improve processing speed. However, modern database environments also introduce challenges involving data consistency, interoperability, security, privacy, distributed transaction management, migration complexity, infrastructure costs, and specialized technical skills. The study concludes that intelligent data management requires an integrated database strategy that combines appropriate architecture, automated management, scalable infrastructure, robust governance, and advanced analytics. Future research should investigate autonomous databases, AI-driven query optimization, self-healing database architectures, privacy-preserving data management, multimodel intelligence, edge databases, and intelligent distributed data systems.
Keywords	Database Technologies, Intelligent Data Management, Cloud Databases, NoSQL, Distributed Databases, Data Analytics, Database Automation, Artificial Intelligence, Data Governance, Information Management
Field	Engineering
Published In	Volume 3, Issue 2, March-April 2021
Published On	2021-03-29

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American International Journal of Advanced Research and Development

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Modern Database Technologies for Intelligent Data Management

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