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Abstract
As data scale and complexity continue to grow, the effective management of large tables in SAP HANA has become essential for organizations seeking to maintain high performance and efficient operations. Large data sets can slow down requests, consume a large amount of memory and increase the complexity of tasks such as backup, recovery and data maintenance. This paper focuses on practical methods for managing large tables in SAP HANA, with special emphasis on partitioning techniques, data storage, and performance optimization. The document describes the different types of tables and partition options in SAP HANA, highlighting the challenges that large tables pose for system performance and memory usage. It then presents best practices for technical tables, methods for purging or storing data, and continuous monitoring methods to ensure that performance stays optimal over time. Based on Gartner's market research for 2023, the study highlights the importance of tailored data management strategies that can adapt to business needs. Following structured partitioning and data management methods, organizations can significantly improve SAP HANA efficiency, reduce system maintenance and support future scalability. These strategies are essential for organizations that rely on SAP HANA to manage vast and growing data sets and ensure stability, speed and reliability.
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