Abstract

This volume of enterprise data has contributed to the realization of the high demand for scalable, elastic, and accessible data platforms, which would enable the organization to entice insights without diminishing the data engineering professionals within the organization. In this study, we provided a building design to build scalable self-service data platforms for enterprise analytics. The suggested architecture unites distributed data processing systems, cloud computing, automated data engineering pipelines, metadata management, data governance services, and user-oriented analytics services on a single platform. It emphasizes the modularity, elasticity, interoperability, security, and practical application of resources to meet a vast array of analytical workloads and ever-increasing data volumes. Self-service enables business analysts, data scientists, and decision-makers to explore, shape, and analyze data and establish visualizations through controlled interfaces and impact data quality, security, and compliance criteria of organizational data. It is also engineered to have automated provisioning, workload coordination, monitoring, and dynamically scalable storage, as well as computing resources to aid more effectively with scaling operations. The storage, computation, governance, and consumption layers employed in the platform are separated and automatically scaled to the workload requirements, which reduces the system overhead in terms of infrastructure and maintenance. The research reveals interesting architectural principles, implementation aspects, and issues about building enterprise-level self-service analytics spaces. The given methodology offers a base to organizations that want to bring data accessibility to democracy, fasten the analytical decision-making process, and build and sustain scalable and sustainable data-driven business operations