Enterprise Reporting Using Data Virtualization and Data Fabric Architecture with SAP Datasphere

Abstract

Traditional enterprise reporting architectures rely on Extract–Transform–Load (ETL) pipelines that replicate large volumes of transactional data into centralized data warehouses or data lakes. Although widely adopted, these approaches introduce significant infrastructure costs, operational complexity, data latency, and governance risks. This paper presents a system and method for enterprise and retail reporting based on a Data Fabric model with data virtualization, implemented using SAP Datasphere. The proposed architecture virtualizes data semantics rather than replicating raw datasets, enabling analytical models to be constructed centrally and governed consistently while retrieving actual data only at report execution time. The system integrates cloud-based and on‑premise enterprise sources, supports modern business intelligence and planning platforms, and enables a phased transition in which SAP BW on HANA is progressively removed from the analytics landscape and retained only for legacy close and financial planning processes. Experimental and operational observations demonstrate that the approach reduces data duplication, improves reporting agility, and strengthens enterprise data governance.

Index Terms— Data Fabric, Data Virtualization, SAP Datasphere, Enterprise Reporting, Retail Analytics, Financial Close, Essbase, Hybrid Analytics.

Enterprise analytics architectures are undergoing a fundamental shift driven by cloud adoption, real-time decision requirements, and stringent data governance regulations. Traditional extract–transform–load (ETL)–centric reporting systems, while mature, struggle to scale efficiently due to data replication, latency, and governance complexity. This paper presents an up-to-date enterprise reporting architecture aligned with current industry and research standards (2024–2025), derived from an original patented system, that leverages data virtualization and Data Fabric principles implemented using SAP Datasphere.

The proposed system virtualizes data semantics rather than physically replicating transactional datasets, enabling analytical models to be centrally governed while accessing actual data only at report execution time. The architecture supports hybrid and multi-cloud enterprise landscapes, integrates advanced automation, extensibility through Python-based dataflows, and interoperability with modern analytics platforms such as Power BI, SAP Analytics Cloud, Tableau, Azure Analysis Services, and OneStream. The approach demonstrates measurable improvements in cost efficiency, reporting agility, security, and regulatory compliance, positioning it as a next-generation alternative to conventional enterprise reporting systems.