The rapid evolution of enterprise architecture necessitates innovative approaches to manage the increasing complexities of digital ecosystems. This paper explores the transformative potential of Artificial Intelligence (AI)-driven cloud solutions in modernizing enterprise architecture, with a focus on integrating DevOps and DataOps methodologies to achieve scalability. AI-powered tools and frameworks in cloud computing offer unparalleled scalability, operational efficiency, and real-time adaptability, enabling enterprises to remain competitive in a data-driven economy. By combining DevOps' focus on streamlining software development and operations with DataOps' emphasis on agile and automated data pipeline management, organizations can optimize workflow automation, accelerate deployment cycles, and enhance decision-making processes. AI further augments this synergy by facilitating predictive analytics, anomaly detection, and intelligent resource allocation, which are critical for achieving scalability and reliability in dynamic business environments. Case studies highlight the successful application of these technologies across various industries, showcasing measurable improvements in performance and cost efficiency. The paper also addresses challenges in adopting AI-driven cloud solutions, including data privacy, compliance, and skill gaps, offering actionable recommendations for mitigating these obstacles. Emphasis is placed on the need for collaborative strategies between IT and business teams to maximize the potential of integrated DevOps and DataOps frameworks.
Fatou Diop· International Journal of Art...· 0 citations
The distributed databases now become a building block of modern computing infrastructures under the influence of the dramatic increase in data volumes, the evolution of cloud computing, and the need to manage large-scale, fault-tolerant, and performance-intensive data warehouses. The conventional centralized database architecture is no longer adequate to support the needs of the modern applications including cloud services, Internet of Things (IoT), big data analytics, real-time processing and globally distributed web applications. Distributed database system handles these issues by partitioning, replicating and controlling data in more geographically dispersed nodes whilst ensuring consistency, availability and reliability. This paper entails a detailed analysis of distributed database in the contemporary computer use. It discusses the architectural concepts, design techniques, data dispersion strategies, and consistency models and techniques of dealing with transaction in a distributed database system. An in-depth literature review reflects the development of distributed databases, the primitive models of client-servers to the new cloud-native and NoSQL databases. The given methodology analyses the system design issues as part of it, such as data partitioning, replication scheme, concurrency control, and fault resistance. The analysis of the performance evaluation is done through experimental assessment and discussion of performance measures which include the latency, throughput, scalability, and availability. The paper has summarized by determining major issues, new trends and subsequent research agenda in the distributed database systems.
Fatou Diop· International Journal of App...· 0 citations
