Which approach should be avoided for optimal performance in SAP HANA?

Transferring large data sets frequently is not an optimal approach for performance in SAP HANA because it can significantly increase network bandwidth usage and processing time, leading to delays in data accessibility and report generation. SAP HANA is designed for high-performance in-memory operations and can handle large volumes of data efficiently when the data is stored and processed locally within the database.

In contrast, using native SQL features, aggregating calculations within the database, and applying indexing on frequently queried columns are all practices that enhance performance. Native SQL features leverage the strengths of the HANA platform to optimize execution plans and reduce computational overhead. Additionally, performing aggregations within the database minimizes the amount of data that needs to be transferred elsewhere, improving response times for analytics. Indexing on frequently queried columns helps speed up data retrieval operations, as it enables faster lookups by allowing the database engine to filter records more efficiently.

Thus, for optimal performance in SAP HANA, it is crucial to minimize the frequent transfer of large datasets and instead focus on leveraging the platform's processing capabilities.