Scalable AI and DevOps Architectures for Big Data Storage and API Led Enterprise Transformation
DOI:
https://doi.org/10.15680/j1951z63Keywords:
Scalable AI architecture, DevOps, big data storage, API-led connectivity, enterprise transformation, cloud-native systems, Kubernetes, data lakes, distributed storage, CI/CD automation, Infrastructure as Code, microservices, real-time analytics, intelligent observability, enterprise integration, digital transformationAbstract
Scalable AI and DevOps architectures are central to modern enterprise transformation, enabling organizations to harness big data, accelerate software delivery, and operationalize intelligent decision-making at scale. By integrating distributed storage systems, data lakes, and real-time streaming platforms with AI-driven analytics, enterprises can process high-volume, high-velocity, and high-variety data efficiently. Cloud-native DevOps practices—leveraging containerization, Kubernetes orchestration, Infrastructure as Code (IaC), and CI/CD automation—ensure resilient, elastic, and continuously deployable systems that support evolving business needs.
API-led connectivity plays a critical role in this transformation by enabling modular integration across applications, data services, and AI models. Through layered API architectures—system APIs, process APIs, and experience APIs—organizations can decouple legacy systems, accelerate innovation, and create reusable digital assets. Intelligent observability, automated scaling, security-by-design principles, and policy-driven governance further enhance reliability and compliance. Together, scalable AI, modern DevOps, big data storage frameworks, and API-led architectures provide a foundation for agile, data-driven enterprises capable of sustained digital growth and operational excellence.
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