Scalable Multi-Tenant Digital Product Analytics and Experimentation Platforms Using Kubernetes–OpenStack Architecture.
DOI:
https://doi.org/10.63084/ejbcer.v1i2.59Keywords:
Digital product analytics,\, A/B testing, experimentation platforms, Kubernetes orchestration, OpenStack, multi-tenancyAbstract
Digital product analytics and experimentation platforms have become foundational to enterprise innovation, enabling continuous learning, rapid decision-making, and large-scale optimization of user experiences. Yet existing research treats experimentation science, analytics infrastructure, and cloud-native orchestration as largely separate domains, limiting theoretical understanding of how scalable experimentation ecosystems function as integrated governance infrastructures. This study proposes a unified architectural framework for scalable, multi-tenant digital product analytics and experimentation built on Kubernetes–OpenStack orchestration. Synthesizing digital infrastructure theory, integrated governance principles, and continuous experimentation as organizational learning, the framework reconceptualizes experimentation platforms as closed-loop sociotechnical systems coordinating data flows, decision processes, and adaptive innovation across enterprise environments. The proposed architecture introduces containerized experimentation pipelines, namespace-isolated multi-tenant analytics services, and CI/CD-driven feature deployment mechanisms capable of supporting thousands of concurrent experiments with sub-minute analytical latency. Empirical performance synthesis indicates improvements of 40–233% in concurrent job execution, up to 85% reduction in experiment deployment time, and substantial gains in resource utilization and operational scalability. Beyond technical performance, the framework demonstrates how cloud-native experimentation infrastructures enhance organizational visibility, accelerate innovation cycles, and strengthen enterprise decision governance. By integrating experimentation methodology, cloud-native architecture, and governance theory, this research advances both theoretical and practical understanding of scalable digital experimentation systems and establishes a foundation for continuously adaptive, data-driven enterprise innovation.
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