Optimizing Data Tiering in SAP HANA using Native Storage Extension (NSE): A Performance Evaluation

This explosive growth in the size of enterprise data places large burdens on in-memory computing systems, especially SAP HANA, which, in the past, has relied heavily on expensive Random Access Memory (RAM) to support real-time performance. In an effort to reduce the cost of operation but not the speed, companies are switching to hybrid data tiering technologies including the SAP HANA Native Storage Extension (NSE), that automatically mark data as hot and warm and move data that is not accessed as frequently to low cost stores. The current paper analyzes the architecture body and deployment methods of NSE. It reviews how it affects its performance parameters, i.e., read/write latency, memory usage, query response time, and scalability in the system. Through controlled experiments and benchmark testing, NSE is given a menial review by the paper on the effectiveness of reducing the Total Cost of Ownership (TCO) within reasonable query throughput maintenance. There is also a paper reviewing such configuration components (buffer cache sizing, page-load behavior, and data classification techniques) and integration considerations with existing SAP HANA installations. Its experiment demonstrates that the run-time execution performance is not significantly reduced by an optimally designed NSE implementation in favor of reducing memory consumption (as much as 40 percent). These results make NSE very useful in areas with vast amounts of semi-active data, such as finance, retail, and manufacturing, where the information can efficiently be kept in warm-tier storage. The paper ends with best-practice recommendations, as well as architectural guidelines, with which IT decision-makers and SAP architects can ensure that NSE becomes a central part of a tiered data strategy.