Optimizing SAP HANA: Balancing Performance and Cost for Enterprise Efficiency

Abstract

SAP HANA, one of the leading in-memory database technologies, is a solution that helps organizations to effectively deal with large amounts of data in both transactional and analytical systems. However, its higher infrastructure and operating costs are a challenge for those organizations that are looking to improve performance without increasing the price. This paper discusses the role of data tiering mechanisms, particularly the Native Storage Extension (NSE), in the improvement of efficiency and performance of the system. It is possible to use hybrid in-memory and disk-based storage strategies to keep the most frequently used data in the high-speed memory and move the less important data to more affordable storage media.

In this research, using practical assessments, case studies, and performance benchmarks across different industries, it was shown that the intelligent data tiering with NSE reduces significantly memory consumption without compromising the query performance. Comparisons between the on-premise and cloud configurations reveal the costs and opportunities for reducing expenses through optimal infrastructure choices. The research also identifies some major implementation challenges including data classification complexity, buffer cache management, and the performance impacts of in-memory and disk-based storage.

The findings of the study have practical implications for organizations that are implementing SAP HANA with NSE, including data analysis, pilot testing, resource allocation, and continuous monitoring to improve efficiency and performance. This paper contributes to the ongoing discussion on enterprise data management, economic growth, and real-time analytics, and the findings from this study will be useful to IT executives, database administrators, and financial managers.